International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
DEVELOPING A NOVEL MULTIDIMENSIONAL MULTIGRANULARITY DATA MINING APPROACH FOR...cscpconf
Data Mining is one of the most significant tools for discovering association patterns that are useful for many knowledge domains. Yet, there are some drawbacks in existing mining techniques. Three main weaknesses of current data-mining techniques are: 1) re-scanning of the entire database must be done whenever new attributes are added. 2) An association rule may be true on a certain granularity but fail on a smaller ones and vise verse. 3) Current methods can only be used to find either frequent rules or infrequent rules, but not both at the same time. This research proposes a novel data schema and an algorithm that solves the above weaknesses while improving on the efficiency and effectiveness of data mining strategies. Crucial mechanisms in each step will be clarified in this paper. Finally, this paper presents experimental results regarding efficiency, scalability, information loss, etc. of the proposed approach to prove its advantages.
Combined mining approach to generate patterns for complex datacsandit
The document discusses a combined mining approach to generate patterns from complex data. It proposes applying a lossy-counting algorithm to each data source to obtain frequent itemsets, then generating combined association rules. It also describes obtaining pair and cluster patterns by considering multiple features, and generating incremental pair and cluster patterns. Further, it combines FP-growth and Bayesian belief networks to classify data and obtain more informative knowledge.
COMBINED MINING APPROACH TO GENERATE PATTERNS FOR COMPLEX DATAcscpconf
In Data mining applications, which often involve complex data like multiple heterogeneous data sources, user preferences, decision-making actions and business impacts etc., the complete useful information cannot be obtained by using single data mining method in the form of informative patterns as that would consume more time and space, if and only if it is possible to join large relevant data sources for discovering patterns consisting of various aspects of useful information. We consider combined mining as an approach for mining informative patterns
from multiple data-sources or multiple-features or by multiple-methods as per the requirements. In combined mining approach, we applied Lossy-counting algorithm on each data-source to get the frequent data item-sets and then get the combined association rules. In multi-feature combined mining approach, we obtained pair patterns and cluster patterns and then generate incremental pair patterns and incremental cluster patterns, which cannot be directly generated by the existing methods. In multi-method combined mining approach, we combine FP-growth and Bayesian Belief Network to make a classifier to get more informative knowledge.
An efficient algorithm for sequence generation in data miningijcisjournal
Data mining is the method or the activity of analyzing data from different perspectives and summarizing it
into useful information. There are several major data mining techniques that have been developed and are
used in the data mining projects which include association, classification, clustering, sequential patterns,
prediction and decision tree. Among different tasks in data mining, sequential pattern mining is one of the
most important tasks. Sequential pattern mining involves the mining of the subsequences that appear
frequently in a set of sequences. It has a variety of applications in several domains such as the analysis of
customer purchase patterns, protein sequence analysis, DNA analysis, gene sequence analysis, web access
patterns, seismologic data and weather observations. Various models and algorithms have been developed
for the efficient mining of sequential patterns in large amount of data. This research paper analyzes the
efficiency of three sequence generation algorithms namely GSP, SPADE and PrefixSpan on a retail dataset
by applying various performance factors. From the experimental results, it is observed that the PrefixSpan
algorithm is more efficient than other two algorithms.
PATTERN GENERATION FOR COMPLEX DATA USING HYBRID MININGIJDKP
This document discusses a hybrid data mining approach called combined mining that can generate informative patterns from complex data sources. It proposes applying three techniques: 1) Using the Lossy-counting algorithm on individual data sources to obtain frequent itemsets, 2) Generating incremental pair and cluster patterns using a multi-feature approach, 3) Combining FP-growth and Bayesian Belief Network using a multi-method approach to generate classifiers. The approach is tested on two datasets to obtain more useful knowledge and the results are compared.
This document describes a proposed modified cluster-based fuzzy-genetic data mining algorithm. The algorithm aims to mine both association rules and membership functions from quantitative transaction data. It uses a genetic algorithm approach that represents each set of membership functions as a chromosome. Chromosomes are clustered using a modified k-means approach to reduce computational costs. The representative chromosome of each cluster is used to calculate fitness values. Offspring are produced through genetic operators and selected through roulette wheel selection. The algorithm iterates until obtaining a set of membership functions with high fitness. These are then used to mine multilevel fuzzy association rules from the transaction data. The algorithm is illustrated through a simple example involving transaction data containing purchases of items like milk, bread, etc
Usage and Research Challenges in the Area of Frequent Pattern in Data MiningIOSR Journals
This document discusses the usage of frequent patterns in data mining, including for association mining, classification, and clustering. It provides background on foundational approaches for mining associations using frequent patterns, such as the Apriori, FP-growth, and ECLAT algorithms. It also discusses how frequent patterns have been used for classification tasks, such as generating discriminative features for training classifiers. Finally, it covers various ways frequent patterns have been applied to clustering problems, such as using frequent itemsets to represent and group documents for clustering. The document provides an overview of the state-of-the-art in applying frequent pattern mining across different data mining applications.
A Survey on Fuzzy Association Rule Mining MethodologiesIOSR Journals
Abstract : Fuzzy association rule mining (Fuzzy ARM) uses fuzzy logic to generate interesting association
rules. These association relationships can help in decision making for the solution of a given problem. Fuzzy
ARM is a variant of classical association rule mining. Classical association rule mining uses the concept of
crisp sets. Because of this reason classical association rule mining has several drawbacks. To overcome those
drawbacks the concept of fuzzy association rule mining came. Today there is a huge number of different types of
fuzzy association rule mining algorithms are present in research works and day by day these algorithms are
getting better. But as the problem domain is also becoming more complex in nature, continuous research work
is still going on. In this paper, we have studied several well-known methodologies and algorithms for fuzzy
association rule mining. Four important methodologies are briefly discussed in this paper which will show the
recent trends and future scope of research in the field of fuzzy association rule mining.
Keywords: Knowledge discovery in databases, Data mining, Fuzzy association rule mining, Classical
association rule mining, Very large datasets, Minimum support, Cardinality, Certainty factor, Redundant rule,
Equivalence , Equivalent rules
DEVELOPING A NOVEL MULTIDIMENSIONAL MULTIGRANULARITY DATA MINING APPROACH FOR...cscpconf
Data Mining is one of the most significant tools for discovering association patterns that are useful for many knowledge domains. Yet, there are some drawbacks in existing mining techniques. Three main weaknesses of current data-mining techniques are: 1) re-scanning of the entire database must be done whenever new attributes are added. 2) An association rule may be true on a certain granularity but fail on a smaller ones and vise verse. 3) Current methods can only be used to find either frequent rules or infrequent rules, but not both at the same time. This research proposes a novel data schema and an algorithm that solves the above weaknesses while improving on the efficiency and effectiveness of data mining strategies. Crucial mechanisms in each step will be clarified in this paper. Finally, this paper presents experimental results regarding efficiency, scalability, information loss, etc. of the proposed approach to prove its advantages.
Combined mining approach to generate patterns for complex datacsandit
The document discusses a combined mining approach to generate patterns from complex data. It proposes applying a lossy-counting algorithm to each data source to obtain frequent itemsets, then generating combined association rules. It also describes obtaining pair and cluster patterns by considering multiple features, and generating incremental pair and cluster patterns. Further, it combines FP-growth and Bayesian belief networks to classify data and obtain more informative knowledge.
COMBINED MINING APPROACH TO GENERATE PATTERNS FOR COMPLEX DATAcscpconf
In Data mining applications, which often involve complex data like multiple heterogeneous data sources, user preferences, decision-making actions and business impacts etc., the complete useful information cannot be obtained by using single data mining method in the form of informative patterns as that would consume more time and space, if and only if it is possible to join large relevant data sources for discovering patterns consisting of various aspects of useful information. We consider combined mining as an approach for mining informative patterns
from multiple data-sources or multiple-features or by multiple-methods as per the requirements. In combined mining approach, we applied Lossy-counting algorithm on each data-source to get the frequent data item-sets and then get the combined association rules. In multi-feature combined mining approach, we obtained pair patterns and cluster patterns and then generate incremental pair patterns and incremental cluster patterns, which cannot be directly generated by the existing methods. In multi-method combined mining approach, we combine FP-growth and Bayesian Belief Network to make a classifier to get more informative knowledge.
An efficient algorithm for sequence generation in data miningijcisjournal
Data mining is the method or the activity of analyzing data from different perspectives and summarizing it
into useful information. There are several major data mining techniques that have been developed and are
used in the data mining projects which include association, classification, clustering, sequential patterns,
prediction and decision tree. Among different tasks in data mining, sequential pattern mining is one of the
most important tasks. Sequential pattern mining involves the mining of the subsequences that appear
frequently in a set of sequences. It has a variety of applications in several domains such as the analysis of
customer purchase patterns, protein sequence analysis, DNA analysis, gene sequence analysis, web access
patterns, seismologic data and weather observations. Various models and algorithms have been developed
for the efficient mining of sequential patterns in large amount of data. This research paper analyzes the
efficiency of three sequence generation algorithms namely GSP, SPADE and PrefixSpan on a retail dataset
by applying various performance factors. From the experimental results, it is observed that the PrefixSpan
algorithm is more efficient than other two algorithms.
PATTERN GENERATION FOR COMPLEX DATA USING HYBRID MININGIJDKP
This document discusses a hybrid data mining approach called combined mining that can generate informative patterns from complex data sources. It proposes applying three techniques: 1) Using the Lossy-counting algorithm on individual data sources to obtain frequent itemsets, 2) Generating incremental pair and cluster patterns using a multi-feature approach, 3) Combining FP-growth and Bayesian Belief Network using a multi-method approach to generate classifiers. The approach is tested on two datasets to obtain more useful knowledge and the results are compared.
This document describes a proposed modified cluster-based fuzzy-genetic data mining algorithm. The algorithm aims to mine both association rules and membership functions from quantitative transaction data. It uses a genetic algorithm approach that represents each set of membership functions as a chromosome. Chromosomes are clustered using a modified k-means approach to reduce computational costs. The representative chromosome of each cluster is used to calculate fitness values. Offspring are produced through genetic operators and selected through roulette wheel selection. The algorithm iterates until obtaining a set of membership functions with high fitness. These are then used to mine multilevel fuzzy association rules from the transaction data. The algorithm is illustrated through a simple example involving transaction data containing purchases of items like milk, bread, etc
Usage and Research Challenges in the Area of Frequent Pattern in Data MiningIOSR Journals
This document discusses the usage of frequent patterns in data mining, including for association mining, classification, and clustering. It provides background on foundational approaches for mining associations using frequent patterns, such as the Apriori, FP-growth, and ECLAT algorithms. It also discusses how frequent patterns have been used for classification tasks, such as generating discriminative features for training classifiers. Finally, it covers various ways frequent patterns have been applied to clustering problems, such as using frequent itemsets to represent and group documents for clustering. The document provides an overview of the state-of-the-art in applying frequent pattern mining across different data mining applications.
A Survey on Fuzzy Association Rule Mining MethodologiesIOSR Journals
Abstract : Fuzzy association rule mining (Fuzzy ARM) uses fuzzy logic to generate interesting association
rules. These association relationships can help in decision making for the solution of a given problem. Fuzzy
ARM is a variant of classical association rule mining. Classical association rule mining uses the concept of
crisp sets. Because of this reason classical association rule mining has several drawbacks. To overcome those
drawbacks the concept of fuzzy association rule mining came. Today there is a huge number of different types of
fuzzy association rule mining algorithms are present in research works and day by day these algorithms are
getting better. But as the problem domain is also becoming more complex in nature, continuous research work
is still going on. In this paper, we have studied several well-known methodologies and algorithms for fuzzy
association rule mining. Four important methodologies are briefly discussed in this paper which will show the
recent trends and future scope of research in the field of fuzzy association rule mining.
Keywords: Knowledge discovery in databases, Data mining, Fuzzy association rule mining, Classical
association rule mining, Very large datasets, Minimum support, Cardinality, Certainty factor, Redundant rule,
Equivalence , Equivalent rules
TEXT CLUSTERING USING INCREMENTAL FREQUENT PATTERN MINING APPROACHIJDKP
Text mining is an emerging research field evolving from information retrieval area. Clustering and
classification are the two approaches in data mining which may also be used to perform text classification
and text clustering. The former is supervised while the later is un-supervised. In this paper, our objective is
to perform text clustering by defining an improved distance metric to compute the similarity between two
text files. We use incremental frequent pattern mining to find frequent items and reduce dimensionality.
The improved distance metric may also be used to perform text classification. The distance metric is
validated for the worst, average and best case situations [15]. The results show the proposed distance
metric outperforms the existing measures.
Enhancement techniques for data warehouse staging areaIJDKP
This document discusses techniques for enhancing the performance of data warehouse staging areas. It proposes two algorithms: 1) A semantics-based extraction algorithm that reduces extraction time by pruning useless data using semantic information. 2) A semantics-based transformation algorithm that similarly aims to reduce transformation time. It also explores three scheduling techniques (FIFO, minimum cost, round robin) for loading data into the data warehouse and experimentally evaluates their performance. The goal is to enhance each stage of the ETL process to maximize overall performance.
With the development of database, the data volume stored in database increases rapidly and in the large
amounts of data much important information is hidden. If the information can be extracted from the
database they will create a lot of profit for the organization. The question they are asking is how to extract
this value. The answer is data mining. There are many technologies available to data mining practitioners,
including Artificial Neural Networks, Genetics, Fuzzy logic and Decision Trees. Many practitioners are
wary of Neural Networks due to their black box nature, even though they have proven themselves in many
situations. This paper is an overview of artificial neural networks and questions their position as a
preferred tool by data mining practitioners.
A Novel Multi- Viewpoint based Similarity Measure for Document ClusteringIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
Principle Component Analysis Based on Optimal Centroid Selection Model for Su...ijtsrd
Clustering a large sparse and large scale data is an open research in the data mining. To discover the significant information through clustering algorithm stands inadequate as most of the data finds to be non actionable. Existing clustering technique is not feasible to time varying data in high dimensional space. Hence Subspace clustering will be answerable to problems in the clustering through incorporation of domain knowledge and parameter sensitive prediction. Sensitiveness of the data is also predicted through thresholding mechanism. The problems of usability and usefulness in 3D subspace clustering are very important issue in subspace clustering. . The Solutions is highly helpful benefit for police departments and law enforcement organisations to better understand stock issues and provide insights that will enable them to track activities, predict the likelihood. Also determining the correct dimension is inconsistent and challenging issue in subspace clustering .In this thesis, we propose Centroid based Subspace Forecasting Framework by constraints is proposed, i.e. must link and must not link with domain knowledge. Unsupervised Subspace clustering algorithm with inbuilt process like inconsistent constraints correlating to dimensions has been resolved through singular value decomposition. Principle component analysis is been used in which condition has been explored to estimate the strength of actionable to be particular attributes and utilizing the domain knowledge to refinement and validating the optimal centroids dynamically. An experimental result proves that proposed framework outperforms other competition subspace clustering technique in terms of efficiency, Fmeasure, parameter insensitiveness and accuracy. G. Raj Kamal | A. Deepika | D. Pavithra | J. Mohammed Nadeem | V. Prasath Kumar "Principle Component Analysis Based on Optimal Centroid Selection Model for SubSpace Clustering Model" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31374.pdf Paper Url :https://www.ijtsrd.com/computer-science/data-miining/31374/principle-component-analysis-based-on-optimal-centroid-selection-model-for-subspace-clustering-model/g-raj-kamal
A CONCEPTUAL METADATA FRAMEWORK FOR SPATIAL DATA WAREHOUSEIJDKP
Metadata represents the information about data to be stored in Data Warehouses. It is a mandatory
element of Data Warehouse to build an efficient Data Warehouse. Metadata helps in data integration,
lineage, data quality and populating transformed data into data warehouse. Spatial data warehouses are
based on spatial data mostly collected from Geographical Information Systems (GIS) and the transactional
systems that are specific to an application or enterprise. Metadata design and deployment is the most
critical phase in building of data warehouse where it is mandatory to bring the spatial information and
data modeling together. In this paper, we present a holistic metadata framework that drives metadata
creation for spatial data warehouse. Theoretically, the proposed metadata framework improves the
efficiency of accessing of data in response to frequent queries on SDWs. In other words, the proposed
framework decreases the response time of the query and accurate information is fetched from Data
Warehouse including the spatial information
This document summarizes an algorithm called the Fuzzy based Optimal Search Space Pruning (FOSSP) for efficiently mining associative rules from large datasets. FOSSP uses parallel pruning techniques at different levels of item sets to improve scalability and reduce execution time compared to existing fuzzy Apriori algorithms. The objectives are to minimize candidate item sets and enhance association rule mining by evaluating maximal information associated with each item. FOSSP analyzes scalability issues and deploys parallel pruning to mine transaction items simultaneously, improving speed. It generates frequent items and rules based on n-level item sets to reduce maintenance compared to sequential algorithms like Apriori.
INTEGRATED ASSOCIATIVE CLASSIFICATION AND NEURAL NETWORK MODEL ENHANCED BY US...IJDKP
The document summarizes a proposed methodology that integrates associative classification and neural networks for improved classification accuracy. It begins by introducing association rule mining and associative classification. It then describes using chi-squared analysis and the Gini index for attribute selection and rule pruning to generate a reduced set of rules. These rules are used to train a backpropagation neural network classifier. The methodology is tested on datasets from a public repository, demonstrating improved accuracy over traditional associative classification alone. Future work to integrate optical neural networks is also proposed.
In the present day huge amount of data is generated in every minute and transferred frequently. Although
the data is sometimes static but most commonly it is dynamic and transactional. New data that is being
generated is getting constantly added to the old/existing data. To discover the knowledge from this
incremental data, one approach is to run the algorithm repeatedly for the modified data sets which is time
consuming. Again to analyze the datasets properly, construction of efficient classifier model is necessary.
The objective of developing such a classifier is to classify unlabeled dataset into appropriate classes. The
paper proposes a dimension reduction algorithm that can be applied in dynamic environment for
generation of reduced attribute set as dynamic reduct, and an optimization algorithm which uses the
reduct and build up the corresponding classification system. The method analyzes the new dataset, when it
becomes available, and modifies the reduct accordingly to fit the entire dataset and from the entire data
set, interesting optimal classification rule sets are generated. The concepts of discernibility relation,
attribute dependency and attribute significance of Rough Set Theory are integrated for the generation of
dynamic reduct set, and optimal classification rules are selected using PSO method, which not only
reduces the complexity but also helps to achieve higher accuracy of the decision system. The proposed
method has been applied on some benchmark dataset collected from the UCI repository and dynamic
reduct is computed, and from the reduct optimal classification rules are also generated. Experimental
result shows the efficiency of the proposed method.
Analysis of Pattern Transformation Algorithms for Sensitive Knowledge Protect...IOSR Journals
The document analyzes pattern transformation algorithms for sensitive knowledge protection in data mining. It discusses:
1) Three main privacy preserving techniques - heuristic, cryptography, and reconstruction-based. The proposed algorithms use heuristic-based techniques.
2) Four proposed heuristic-based algorithms - item-based Maxcover (IMA), pattern-based Maxcover (PMA), transaction-based Maxcover (TMA), and Sensitivity Cost Sanitization (SCS) - that modify sensitive transactions to decrease support of restrictive patterns.
3) Performance improvements including parallel and incremental approaches to handle large, dynamic databases while balancing privacy and utility.
Particle Swarm Optimization based K-Prototype Clustering Algorithm iosrjce
This document summarizes a research paper that proposes a new Particle Swarm Optimization (PSO) based K-Prototype clustering algorithm to cluster mixed numeric and categorical data. It begins with background information on clustering algorithms like K-Means, K-Modes, and K-Prototype. It then describes the K-Prototype algorithm, PSO, and discrete binary PSO. Related work integrating PSO with other clustering algorithms is also reviewed. The proposed approach uses binary PSO to select improved initial prototypes for K-Prototype clustering in order to obtain better clustering results than traditional K-Prototype and avoid local optima.
A SURVEY ON OPTIMIZATION APPROACHES TO TEXT DOCUMENT CLUSTERINGijcsa
This document provides a survey of optimization approaches that have been applied to text document clustering. It discusses several clustering algorithms and categorizes them as partitioning methods, hierarchical methods, density-based methods, grid-based methods, model-based methods, frequent pattern-based clustering, and constraint-based clustering. It then describes several soft computing techniques that have been used as optimization approaches for text document clustering, including genetic algorithms, bees algorithms, particle swarm optimization, and ant colony optimization. These optimization techniques perform a global search to improve the quality and efficiency of document clustering algorithms.
One of the most important problems in modern finance is finding efficient ways to summarize and visualize
the stock market data to give individuals or institutions useful information about the market behavior for
investment decisions Therefore, Investment can be considered as one of the fundamental pillars of national
economy. So, at the present time many investors look to find criterion to compare stocks together and
selecting the best and also investors choose strategies that maximize the earning value of the investment
process. Therefore the enormous amount of valuable data generated by the stock market has attracted
researchers to explore this problem domain using different methodologies. Therefore research in data
mining has gained a high attraction due to the importance of its applications and the increasing generation
information. So, Data mining tools such as association rule, rule induction method and Apriori algorithm
techniques are used to find association between different scripts of stock market, and also much of the
research and development has taken place regarding the reasons for fluctuating Indian stock exchange.
But, now days there are two important factors such as gold prices and US Dollar Prices are more
dominating on Indian Stock Market and to find out the correlation between gold prices, dollar prices and
BSE index statistical correlation is used and this helps the activities of stock operators, brokers, investors
and jobbers. They are based on the forecasting the fluctuation of index share prices, gold prices, dollar
prices and transactions of customers. Hence researcher has considered these problems as a topic for
research.
SPATIAL R-TREE INDEX BASED ON GRID DIVISION FOR QUERY PROCESSINGijdms
Tracing moving objects have turned out to be essential in our life and have a lot of uses like: GPS guide,
traffic monitor based administrations and location-based services. Tracking the changing places of objects
has turned into important issues. The moving entities send their positions to the server through a system
and large amount of data is generated from these objects with high frequent updates so we need an index
structure to retrieve information as fast as possible. The index structure should be adaptive, dynamic to
monitor the locations of objects and quick to give responses to the inquiries efficiently. The most wellknown
kinds of queries strategies in moving objects databases are Rang, Point and K-Nearest Neighbour
and inquiries. This study uses R-tree method to get detailed range query results efficiently. But using R-tree
only will generate much overlapping and coverage between MBR. So R-tree by combining with Gridpartition
index is used because grid-index can reduce the overlap and coverage between MBR. The query
performance will be efficient by using these methods. We perform an extensive experimental study to
compare the two approaches on modern hardware.
New proximity estimate for incremental update of non uniformly distributed cl...IJDKP
The conventional clustering algorithms mine static databases and generate a set of patterns in the form of
clusters. Many real life databases keep growing incrementally. For such dynamic databases, the patterns
extracted from the original database become obsolete. Thus the conventional clustering algorithms are not
suitable for incremental databases due to lack of capability to modify the clustering results in accordance
with recent updates. In this paper, the author proposes a new incremental clustering algorithm called
CFICA(Cluster Feature-Based Incremental Clustering Approach for numerical data) to handle numerical
data and suggests a new proximity metric called Inverse Proximity Estimate (IPE) which considers the
proximity of a data point to a cluster representative as well as its proximity to a farthest point in its vicinity.
CFICA makes use of the proposed proximity metric to determine the membership of a data point into a
cluster.
An Effective Heuristic Approach for Hiding Sensitive Patterns in DatabasesIOSR Journals
The document presents an algorithm for sanitizing a database to hide sensitive patterns while minimizing changes to the original data. It identifies sensitive transactions containing restrictive patterns to be hidden and sorts them by degree and size. It then selects items with the maximum cover across restrictive patterns and removes them from sensitive transactions, reducing support for the patterns. This process iterates until restrictive pattern support is reduced to 0. The sanitized database combines modified sensitive transactions with unmodified non-sensitive transactions. The algorithm is tested on sample databases to evaluate effectiveness with minimal impact on the original data.
Mining Of Big Data Using Map-Reduce TheoremIOSR Journals
This document discusses using MapReduce to efficiently extract large and complex data from big data sources. It proposes a MapReduce theorem for big data mining that is more efficient than the Heterogeneous Autonomous Complex and Evolving (HACE) theorem. MapReduce libraries support different programming languages and platforms, allowing for portable big data processing. The document outlines how MapReduce connects to Big Query to allow SQL queries to efficiently extract and analyze large datasets stored in the cloud. It also discusses data cleaning, sampling, and normalization as part of the big data mining process.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The document discusses web mining techniques for web personalization. It defines web mining as extracting useful information from web data, including web usage mining, web content mining, and web structure mining. Web usage mining involves data gathering, preparation, pattern discovery, analysis, visualization and application. Web content mining extracts information from web document contents. The document then discusses how these web mining techniques can be applied to web personalization by learning about user interactions and interests to customize web page content and presentations.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
TEXT CLUSTERING USING INCREMENTAL FREQUENT PATTERN MINING APPROACHIJDKP
Text mining is an emerging research field evolving from information retrieval area. Clustering and
classification are the two approaches in data mining which may also be used to perform text classification
and text clustering. The former is supervised while the later is un-supervised. In this paper, our objective is
to perform text clustering by defining an improved distance metric to compute the similarity between two
text files. We use incremental frequent pattern mining to find frequent items and reduce dimensionality.
The improved distance metric may also be used to perform text classification. The distance metric is
validated for the worst, average and best case situations [15]. The results show the proposed distance
metric outperforms the existing measures.
Enhancement techniques for data warehouse staging areaIJDKP
This document discusses techniques for enhancing the performance of data warehouse staging areas. It proposes two algorithms: 1) A semantics-based extraction algorithm that reduces extraction time by pruning useless data using semantic information. 2) A semantics-based transformation algorithm that similarly aims to reduce transformation time. It also explores three scheduling techniques (FIFO, minimum cost, round robin) for loading data into the data warehouse and experimentally evaluates their performance. The goal is to enhance each stage of the ETL process to maximize overall performance.
With the development of database, the data volume stored in database increases rapidly and in the large
amounts of data much important information is hidden. If the information can be extracted from the
database they will create a lot of profit for the organization. The question they are asking is how to extract
this value. The answer is data mining. There are many technologies available to data mining practitioners,
including Artificial Neural Networks, Genetics, Fuzzy logic and Decision Trees. Many practitioners are
wary of Neural Networks due to their black box nature, even though they have proven themselves in many
situations. This paper is an overview of artificial neural networks and questions their position as a
preferred tool by data mining practitioners.
A Novel Multi- Viewpoint based Similarity Measure for Document ClusteringIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
Principle Component Analysis Based on Optimal Centroid Selection Model for Su...ijtsrd
Clustering a large sparse and large scale data is an open research in the data mining. To discover the significant information through clustering algorithm stands inadequate as most of the data finds to be non actionable. Existing clustering technique is not feasible to time varying data in high dimensional space. Hence Subspace clustering will be answerable to problems in the clustering through incorporation of domain knowledge and parameter sensitive prediction. Sensitiveness of the data is also predicted through thresholding mechanism. The problems of usability and usefulness in 3D subspace clustering are very important issue in subspace clustering. . The Solutions is highly helpful benefit for police departments and law enforcement organisations to better understand stock issues and provide insights that will enable them to track activities, predict the likelihood. Also determining the correct dimension is inconsistent and challenging issue in subspace clustering .In this thesis, we propose Centroid based Subspace Forecasting Framework by constraints is proposed, i.e. must link and must not link with domain knowledge. Unsupervised Subspace clustering algorithm with inbuilt process like inconsistent constraints correlating to dimensions has been resolved through singular value decomposition. Principle component analysis is been used in which condition has been explored to estimate the strength of actionable to be particular attributes and utilizing the domain knowledge to refinement and validating the optimal centroids dynamically. An experimental result proves that proposed framework outperforms other competition subspace clustering technique in terms of efficiency, Fmeasure, parameter insensitiveness and accuracy. G. Raj Kamal | A. Deepika | D. Pavithra | J. Mohammed Nadeem | V. Prasath Kumar "Principle Component Analysis Based on Optimal Centroid Selection Model for SubSpace Clustering Model" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31374.pdf Paper Url :https://www.ijtsrd.com/computer-science/data-miining/31374/principle-component-analysis-based-on-optimal-centroid-selection-model-for-subspace-clustering-model/g-raj-kamal
A CONCEPTUAL METADATA FRAMEWORK FOR SPATIAL DATA WAREHOUSEIJDKP
Metadata represents the information about data to be stored in Data Warehouses. It is a mandatory
element of Data Warehouse to build an efficient Data Warehouse. Metadata helps in data integration,
lineage, data quality and populating transformed data into data warehouse. Spatial data warehouses are
based on spatial data mostly collected from Geographical Information Systems (GIS) and the transactional
systems that are specific to an application or enterprise. Metadata design and deployment is the most
critical phase in building of data warehouse where it is mandatory to bring the spatial information and
data modeling together. In this paper, we present a holistic metadata framework that drives metadata
creation for spatial data warehouse. Theoretically, the proposed metadata framework improves the
efficiency of accessing of data in response to frequent queries on SDWs. In other words, the proposed
framework decreases the response time of the query and accurate information is fetched from Data
Warehouse including the spatial information
This document summarizes an algorithm called the Fuzzy based Optimal Search Space Pruning (FOSSP) for efficiently mining associative rules from large datasets. FOSSP uses parallel pruning techniques at different levels of item sets to improve scalability and reduce execution time compared to existing fuzzy Apriori algorithms. The objectives are to minimize candidate item sets and enhance association rule mining by evaluating maximal information associated with each item. FOSSP analyzes scalability issues and deploys parallel pruning to mine transaction items simultaneously, improving speed. It generates frequent items and rules based on n-level item sets to reduce maintenance compared to sequential algorithms like Apriori.
INTEGRATED ASSOCIATIVE CLASSIFICATION AND NEURAL NETWORK MODEL ENHANCED BY US...IJDKP
The document summarizes a proposed methodology that integrates associative classification and neural networks for improved classification accuracy. It begins by introducing association rule mining and associative classification. It then describes using chi-squared analysis and the Gini index for attribute selection and rule pruning to generate a reduced set of rules. These rules are used to train a backpropagation neural network classifier. The methodology is tested on datasets from a public repository, demonstrating improved accuracy over traditional associative classification alone. Future work to integrate optical neural networks is also proposed.
In the present day huge amount of data is generated in every minute and transferred frequently. Although
the data is sometimes static but most commonly it is dynamic and transactional. New data that is being
generated is getting constantly added to the old/existing data. To discover the knowledge from this
incremental data, one approach is to run the algorithm repeatedly for the modified data sets which is time
consuming. Again to analyze the datasets properly, construction of efficient classifier model is necessary.
The objective of developing such a classifier is to classify unlabeled dataset into appropriate classes. The
paper proposes a dimension reduction algorithm that can be applied in dynamic environment for
generation of reduced attribute set as dynamic reduct, and an optimization algorithm which uses the
reduct and build up the corresponding classification system. The method analyzes the new dataset, when it
becomes available, and modifies the reduct accordingly to fit the entire dataset and from the entire data
set, interesting optimal classification rule sets are generated. The concepts of discernibility relation,
attribute dependency and attribute significance of Rough Set Theory are integrated for the generation of
dynamic reduct set, and optimal classification rules are selected using PSO method, which not only
reduces the complexity but also helps to achieve higher accuracy of the decision system. The proposed
method has been applied on some benchmark dataset collected from the UCI repository and dynamic
reduct is computed, and from the reduct optimal classification rules are also generated. Experimental
result shows the efficiency of the proposed method.
Analysis of Pattern Transformation Algorithms for Sensitive Knowledge Protect...IOSR Journals
The document analyzes pattern transformation algorithms for sensitive knowledge protection in data mining. It discusses:
1) Three main privacy preserving techniques - heuristic, cryptography, and reconstruction-based. The proposed algorithms use heuristic-based techniques.
2) Four proposed heuristic-based algorithms - item-based Maxcover (IMA), pattern-based Maxcover (PMA), transaction-based Maxcover (TMA), and Sensitivity Cost Sanitization (SCS) - that modify sensitive transactions to decrease support of restrictive patterns.
3) Performance improvements including parallel and incremental approaches to handle large, dynamic databases while balancing privacy and utility.
Particle Swarm Optimization based K-Prototype Clustering Algorithm iosrjce
This document summarizes a research paper that proposes a new Particle Swarm Optimization (PSO) based K-Prototype clustering algorithm to cluster mixed numeric and categorical data. It begins with background information on clustering algorithms like K-Means, K-Modes, and K-Prototype. It then describes the K-Prototype algorithm, PSO, and discrete binary PSO. Related work integrating PSO with other clustering algorithms is also reviewed. The proposed approach uses binary PSO to select improved initial prototypes for K-Prototype clustering in order to obtain better clustering results than traditional K-Prototype and avoid local optima.
A SURVEY ON OPTIMIZATION APPROACHES TO TEXT DOCUMENT CLUSTERINGijcsa
This document provides a survey of optimization approaches that have been applied to text document clustering. It discusses several clustering algorithms and categorizes them as partitioning methods, hierarchical methods, density-based methods, grid-based methods, model-based methods, frequent pattern-based clustering, and constraint-based clustering. It then describes several soft computing techniques that have been used as optimization approaches for text document clustering, including genetic algorithms, bees algorithms, particle swarm optimization, and ant colony optimization. These optimization techniques perform a global search to improve the quality and efficiency of document clustering algorithms.
One of the most important problems in modern finance is finding efficient ways to summarize and visualize
the stock market data to give individuals or institutions useful information about the market behavior for
investment decisions Therefore, Investment can be considered as one of the fundamental pillars of national
economy. So, at the present time many investors look to find criterion to compare stocks together and
selecting the best and also investors choose strategies that maximize the earning value of the investment
process. Therefore the enormous amount of valuable data generated by the stock market has attracted
researchers to explore this problem domain using different methodologies. Therefore research in data
mining has gained a high attraction due to the importance of its applications and the increasing generation
information. So, Data mining tools such as association rule, rule induction method and Apriori algorithm
techniques are used to find association between different scripts of stock market, and also much of the
research and development has taken place regarding the reasons for fluctuating Indian stock exchange.
But, now days there are two important factors such as gold prices and US Dollar Prices are more
dominating on Indian Stock Market and to find out the correlation between gold prices, dollar prices and
BSE index statistical correlation is used and this helps the activities of stock operators, brokers, investors
and jobbers. They are based on the forecasting the fluctuation of index share prices, gold prices, dollar
prices and transactions of customers. Hence researcher has considered these problems as a topic for
research.
SPATIAL R-TREE INDEX BASED ON GRID DIVISION FOR QUERY PROCESSINGijdms
Tracing moving objects have turned out to be essential in our life and have a lot of uses like: GPS guide,
traffic monitor based administrations and location-based services. Tracking the changing places of objects
has turned into important issues. The moving entities send their positions to the server through a system
and large amount of data is generated from these objects with high frequent updates so we need an index
structure to retrieve information as fast as possible. The index structure should be adaptive, dynamic to
monitor the locations of objects and quick to give responses to the inquiries efficiently. The most wellknown
kinds of queries strategies in moving objects databases are Rang, Point and K-Nearest Neighbour
and inquiries. This study uses R-tree method to get detailed range query results efficiently. But using R-tree
only will generate much overlapping and coverage between MBR. So R-tree by combining with Gridpartition
index is used because grid-index can reduce the overlap and coverage between MBR. The query
performance will be efficient by using these methods. We perform an extensive experimental study to
compare the two approaches on modern hardware.
New proximity estimate for incremental update of non uniformly distributed cl...IJDKP
The conventional clustering algorithms mine static databases and generate a set of patterns in the form of
clusters. Many real life databases keep growing incrementally. For such dynamic databases, the patterns
extracted from the original database become obsolete. Thus the conventional clustering algorithms are not
suitable for incremental databases due to lack of capability to modify the clustering results in accordance
with recent updates. In this paper, the author proposes a new incremental clustering algorithm called
CFICA(Cluster Feature-Based Incremental Clustering Approach for numerical data) to handle numerical
data and suggests a new proximity metric called Inverse Proximity Estimate (IPE) which considers the
proximity of a data point to a cluster representative as well as its proximity to a farthest point in its vicinity.
CFICA makes use of the proposed proximity metric to determine the membership of a data point into a
cluster.
An Effective Heuristic Approach for Hiding Sensitive Patterns in DatabasesIOSR Journals
The document presents an algorithm for sanitizing a database to hide sensitive patterns while minimizing changes to the original data. It identifies sensitive transactions containing restrictive patterns to be hidden and sorts them by degree and size. It then selects items with the maximum cover across restrictive patterns and removes them from sensitive transactions, reducing support for the patterns. This process iterates until restrictive pattern support is reduced to 0. The sanitized database combines modified sensitive transactions with unmodified non-sensitive transactions. The algorithm is tested on sample databases to evaluate effectiveness with minimal impact on the original data.
Mining Of Big Data Using Map-Reduce TheoremIOSR Journals
This document discusses using MapReduce to efficiently extract large and complex data from big data sources. It proposes a MapReduce theorem for big data mining that is more efficient than the Heterogeneous Autonomous Complex and Evolving (HACE) theorem. MapReduce libraries support different programming languages and platforms, allowing for portable big data processing. The document outlines how MapReduce connects to Big Query to allow SQL queries to efficiently extract and analyze large datasets stored in the cloud. It also discusses data cleaning, sampling, and normalization as part of the big data mining process.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The document discusses web mining techniques for web personalization. It defines web mining as extracting useful information from web data, including web usage mining, web content mining, and web structure mining. Web usage mining involves data gathering, preparation, pattern discovery, analysis, visualization and application. Web content mining extracts information from web document contents. The document then discusses how these web mining techniques can be applied to web personalization by learning about user interactions and interests to customize web page content and presentations.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Lectura d disposicion vs norma riccardo guastiniTere Tapia
La Unión Europea ha acordado un embargo petrolero contra Rusia en respuesta a la invasión de Ucrania. El embargo prohibirá la mayor parte de las importaciones de petróleo ruso a la UE y se implementará de manera gradual durante los próximos seis meses. Algunos países de la UE aún dependen en gran medida del petróleo ruso y se les ha otorgado una exención temporal, pero se espera que todos los estados miembros de la UE dejen de importar petróleo ruso para fines de 2022.
El aprendizaje invertido es un enfoque pedagógico donde los estudiantes reciben la información fuera del aula a través de videos, y el tiempo en clase se utiliza para retroalimentar el material y hacer que el aprendizaje sea más significativo y personalizado. Los profesores deben asumir un nuevo rol como guías para ayudar a los estudiantes a aplicar la teoría aprendida fuera de clase. Aunque este enfoque mejora la interacción entre estudiantes y profesores, también es importante no descuidar la investigación cuantitat
Este documento explora el tema de la publicidad, definiéndola de varias maneras y discutiendo tanto sus ventajas como desventajas. También analiza cómo la publicidad funciona para fabricantes, empresas de servicios y desde perspectivas económica, conservadora e informativa.
Windows 7 es la versión más reciente del sistema operativo Windows de Microsoft. Incluye mejoras como un mejor reconocimiento de escritura a mano y soporte para discos duros virtuales. Proporciona compatibilidad con una variedad de dispositivos como PC, portátiles y tabletas.
La Unión Europea ha propuesto un nuevo paquete de sanciones contra Rusia que incluye un embargo al petróleo. El embargo prohibiría las importaciones de petróleo ruso por mar y por oleoducto, aunque se concederían exenciones temporales a Hungría y Eslovaquia. El objetivo es aumentar la presión económica sobre Rusia para que ponga fin a su invasión de Ucrania.
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La madre le desea a su hija lo mejor en este nuevo capítulo de su vida como mujer joven que empieza su propio camino. Recuerda con cariño cuando la llevó dentro de su vientre y la cuidó con amor y ternura. Le desea suerte en este nuevo sendero y que encuentre la felicidad, contando siempre con el apoyo de su familia. La hija es el tesoro más preciado para su madre.
MAP/REDUCE DESIGN AND IMPLEMENTATION OF APRIORIALGORITHM FOR HANDLING VOLUMIN...acijjournal
Apriori is one of the key algorithms to generate frequent itemsets. Analysing frequent itemset is a crucial
step in analysing structured data and in finding association relationship between items. This stands as an
elementary foundation to supervised learning, which encompasses classifier and feature extraction
methods. Applying this algorithm is crucial to understand the behaviour of structured data. Most of the
structured data in scientific domain are voluminous. Processing such kind of data requires state of the art
computing machines. Setting up such an infrastructure is expensive. Hence a distributed environment
such as a clustered setup is employed for tackling such scenarios. Apache Hadoop distribution is one of
the cluster frameworks in distributed environment that helps by distributing voluminous data across a
number of nodes in the framework. This paper focuses on map/reduce design and implementation of
Apriori algorithm for structured data analysis.
An Efficient Compressed Data Structure Based Method for Frequent Item Set Miningijsrd.com
Frequent pattern mining is very important for business organizations. The major applications of frequent pattern mining include disease prediction and analysis, rain forecasting, profit maximization, etc. In this paper, we are presenting a new method for mining frequent patterns. Our method is based on a new compact data structure. This data structure will help in reducing the execution time.
This document summarizes an article that proposes an improved association rule mining method using correlation techniques. It integrates positive and negative rule mining concepts with frequent pattern mining to generate unique rules for classification without ambiguity. The method first constructs an FP-tree to efficiently find frequent itemsets. It then uses correlations between itemsets and class labels to determine whether rules belong to the positive or negative rule set. Classification of new data is performed by applying the matching rule subsets. The proposed method aims to address issues with traditional association rule and frequent pattern mining approaches for large datasets.
Frequent pattern mining techniques helpful to find interesting trends or patterns in
massive data. Prior domain knowledge leads to decide appropriate minimum support threshold. This
review article show different frequent pattern mining techniques based on apriori or FP-tree or user
define techniques under different computing environments like parallel, distributed or available data
mining tools, those helpful to determine interesting frequent patterns/itemsets with or without prior
domain knowledge. Proposed review article helps to develop efficient and scalable frequent pattern
mining techniques.
A Quantified Approach for large Dataset Compression in Association MiningIOSR Journals
Abstract: With the rapid development of computer and information technology in the last several decades, an
enormous amount of data in science and engineering will continuously be generated in massive scale; data
compression is needed to reduce the cost and storage space. Compression and discovering association rules by
identifying relationships among sets of items in a transaction database is an important problem in Data Mining.
Finding frequent itemsets is computationally the most expensive step in association rule discovery and therefore
it has attracted significant research attention. However, existing compression algorithms are not appropriate in
data mining for large data sets. In this research a new approach is describe in which the original dataset is
sorted in lexicographical order and desired number of groups are formed to generate the quantification tables.
These quantification tables are used to generate the compressed dataset, which is more efficient algorithm for
mining complete frequent itemsets from compressed dataset. The experimental results show that the proposed
algorithm performs better when comparing it with the mining merge algorithm with different supports and
execution time.
Keywords: Apriori Algorithm, mining merge Algorithm, quantification table
A Framework for Automated Association Mining Over Multiple DatabasesGurdal Ertek
Literature on association mining, the data mining methodology that investigates associations between items, has primarily focused on efficiently mining larger databases. The motivation for association mining is to use the rules obtained from historical data to influence future transactions. However, associations in transactional processes change significantly over time, implying that rules extracted for a given time interval may not be applicable for a later time interval. Hence, an
analysis framework is necessary to identify how associations change over time. This paper presents such a framework, reports the implementation of the framework as a tool, and demonstrates the applicability of and the necessity for the framework through a case study in the domain of finance.
http://research.sabanciuniv.edu.
Data mining is a very popular research topic over the years. Sequential pattern mining or sequential rule mining is very useful application of data mining for the prediction purpose. In this paper, we have presented a review over sequential rule cum sequential pattern mining. The advantages & drawbacks of each popular sequential mining method is discussed in brief.
A literature review of modern association rule mining techniquesijctet
This document discusses association rule mining techniques for extracting useful patterns from large datasets. It provides background on association rule mining and defines key concepts like support, confidence and frequent itemsets. The document then reviews several classic association rule mining algorithms like AIS, Apriori and FP-Growth. It explains that these algorithms aim to improve quality and efficiency by reducing database scans, generating fewer candidate itemsets and using pruning techniques.
This document describes a proposed Optimal Frequent Patterns System (OFPS) that uses a genetic algorithm to discover optimal frequent patterns from transactional databases more efficiently. The OFPS is a three-fold system that first prepares data through cleaning, integration and transformation. It then constructs a Frequent Pattern Tree to discover frequent patterns. Finally, it applies a genetic algorithm to generate optimal frequent patterns, simulating biological evolution to find the best solutions. The proposed system aims to overcome limitations of conventional association rule mining approaches and efficiently discover optimal patterns from large, changing datasets.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Mining High Utility Patterns in Large Databases using Mapreduce FrameworkIRJET Journal
This document discusses mining high utility patterns from large databases using the MapReduce framework. It proposes using the d2HUP algorithm to efficiently mine high utility patterns from partitioned big data in parallel. The algorithm traverses a reverse set enumeration tree using depth-first search to identify high utility patterns based on a minimum utility threshold, without generating candidates. It partitions the data using MapReduce and mines patterns from each partition individually. The results are then combined to obtain the final high utility patterns. The proposed approach aims to improve efficiency over existing methods that are not scalable to large datasets.
This document summarizes a paper that presents a novel method for passive resource discovery in cluster grid environments. The method monitors network packet frequency from nodes' network interface cards to identify nodes with available CPU cycles (<70% utilization) by detecting latency signatures from frequent context switching. Experiments on a 50-node testbed showed the method can consistently and accurately discover available resources by analyzing existing network traffic, including traffic passed through a switch. The paper also proposes algorithms for distributed two-level resource discovery, replication and utilization to optimize resource allocation and access costs in distributed computing environments.
Re-Mining Association Mining Results Through Visualization, Data Envelopment ...ertekg
İndirmek için Bağlantı > https://ertekprojects.com/gurdal-ertek-publications/blog/re-mining-association-mining-results-through-visualization-data-envelopment-analysis-and-decision-trees/
Re-mining is a general framework which suggests the execution of additional data mining steps based on the results of an original data mining process. This study investigates the multi-faceted re-mining of association mining results, develops and presents a practical methodology, and shows the applicability of the developed methodology through real world data. The methodology suggests re-mining using data visualization, data envelopment analysis, and decision trees. Six hypotheses, regarding how re-mining can be carried out on association mining results, are answered in the case study through empirical analysis.
Multiple Minimum Support Implementations with Dynamic Matrix Apriori Algorith...ijsrd.com
Data mining can be defined as the process of uncovering hidden patterns in random data that are potentially useful. The discovery of interesting association relationships among large amounts of business transactions is currently vital for making appropriate business decisions. Association rule analysis is the task of discovering association rules that occur frequently in a given transaction data set. Its task is to find certain relationships among a set of data (itemset) in the database. It has two measurements: Support and confidence values. Confidence value is a measure of rule’s strength, while support value corresponds to statistical significance. There are currently a variety of algorithms to discover association rules. Some of these algorithms depend on the use of minimum support to weed out the uninteresting rules. Other algorithms look for highly correlated items, that is, rules with high confidence. Traditional association rule mining techniques employ predefined support and confidence values. However, specifying minimum support value of the mined rules in advance often leads to either too many or too few rules, which negatively impacts the performance of the overall system. This work proposes a way to efficiently mine association rules over dynamic databases using Dynamic Matrix Apriori technique and Multiple Support Apriori (MSApriori). A modification for Matrix Apriori algorithm to accommodate this modification is proposed. Experiments on large set of data bases have been conducted to validate the proposed framework. The achieved results show that there is a remarkable improvement in the overall performance of the system in terms of run time, the number of generated rules, and number of frequent items used.
Optimization of resource allocation in computational gridsijgca
The resource allocation in Grid computing system needs to be scalable, reliable and smart. It should also be adaptable to change its allocation mechanism depending upon the environment and user’s requirements. Therefore, a scalable and optimized approach for resource allocation where the system can adapt itself to the changing environment and the fluctuating resources is essentially needed. In this paper, a Teaching Learning based optimization approach for resource allocation in Computational Grids is proposed. The proposed algorithm is found to outperform the existing ones in terms of execution time and cost. The algorithm is simulated using GRIDSIM and the simulation results are presented.
The document discusses optimization of resource allocation in computational grids. It proposes using a Teaching-Learning Based Optimization (TLBO) approach for resource allocation. The TLBO algorithm is found to outperform existing algorithms like Ant Colony Optimization, Genetic Algorithm, and Particle Swarm Optimization in terms of execution time and cost. The algorithm is simulated using GRIDSIM and results are presented. Existing resource allocation strategies in computational grids are also reviewed, including static and dynamic approaches as well as auction/market-based models.
IRJET-Efficient Data Linkage Technique using one Class Clustering Tree for Da...IRJET Journal
This document proposes a new one-to-many data linkage technique using a One-Class Clustering Tree (OCCT) to link records from different datasets. The technique constructs a decision tree where internal nodes represent attributes from the first dataset and leaves represent attributes from the second dataset that match. It uses maximum likelihood estimation for splitting criteria and pre-pruning to reduce complexity. The method is applied to the database misuse domain to identify common and malicious users by analyzing access request contexts and accessible data. Evaluation shows the technique achieves better precision and recall than existing methods.
The document proposes an algorithm called MSApriori_VDB for efficiently mining rare association rules from transactional databases. It first converts the transaction database to a vertical data format to reduce the number of scans. It then uses a multiple minimum support framework where each item is assigned a minimum item support based on its frequency. The algorithm generates candidate itemsets, calculates their support, and prunes uninteresting itemsets to identify interesting rare associations with high confidence. Experimental results show the algorithm outperforms previous approaches in memory usage and runtime.
The document presents a proposed algorithm called MSApriori_VDB for efficiently mining rare association rules from transactional databases. The algorithm first converts the transaction database to a vertical data format to reduce the number of scans. It then uses a multiple minimum support framework where each item is assigned a minimum item support based on its frequency. The algorithm generates candidate itemsets, calculates their support, and prunes uninteresting itemsets to identify interesting rare associations with high confidence. Experimental results show the algorithm outperforms previous approaches in memory usage and runtime.
Re-Mining Association Mining Results through Visualization, Data Envelopment ...Gurdal Ertek
Re-mining is a general framework which suggests the execution of additional data mining steps based on the results of an original data mining process. This study investigates the multi-faceted re-mining of association mining results, develops and presents a practical methodology, and shows the applicability of the developed methodology through real world data. The methodology suggests re-mining using data visualization, data envelopment analysis, and decision trees. Six hypotheses, regarding how re-mining can be carried out on association mining results, are answered in the case study through empirical analysis.
http://research.sabanciuniv.edu.
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
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Multidimensional Data Mining to Determine Association Rules in
an Assortment of Granularities
C. Usha Rani 1, B. Rupa Devi 2
1, 2 Asst. Professor of Department of CSE
ABSTRACT
Data Mining is one of the most significant tools for discovering association patterns that are useful for
many knowledge domains. Yet, there are some drawbacks in existing mining techniques. The three main
weaknesses of current data- mining techniques are: 1) rescanning of the entire database must be done
whenever new attributes are added because current methods are based on flat mining using predefined
schemata. 2) An association rule may be true on a certain granularity but fail on a smaller ones and vise
verse. This may result in loss of important association rules. 3) Current methods can only be used to
find either frequent rules or infrequent rules, but not both at the same time.
This research proposes a novel data schema and an algorithm that solves the above weaknesses while
improving on the efficiency and effectiveness of data mining strategies. Crucial mechanisms in each step
will be clarified in this paper. This paper also presents a benchmark which is used to compare the level of
efficiency and effectiveness of the proposed algorithm against other known methods. Finally, this paper
presents experimental results regarding efficiency, scalability, information loss, etc. of
the proposed approach to prove its advantages.
Keywords - Multidimensional Data Mining; Granular Computing; Apriori Algorithm; Concept
Taxonomy; Association Rule
I. INTRODUCTION
The scientific and business communities are
increasingly interested in knowledge discovery.
Significant examples are finding new drugs for cancers
and new portfolios of products/services. The notion of
association rule is capable of providing simple but
useful form of knowledge [4,6]. Thereafter, methods
for discovery of association rules such as machine
learning and data mining have been extensively
studied.
Most of the conventional mining
approaches only perform flat scan over the databank
based on a pre-defined schema. Most associations
occur in a context of certain breadth, the knowledge
usually encompasses multidimensional content.
However, adding attributes to the mining task means
changing the schema and rescanning is required. This
is highly inefficient.
The second problem is most conventional
mining approaches assume that the induced rules
should be effective throughout a database as a whole.
This obviously does not fit with real-life cases [6].
Different association rules can be found in different
parts (segments) of database. If a mining tool deals
only with the database as a whole, the meaningful at
smaller granularities will be lost.
The goal of this research is to provide an
approach with novel data structure and efficient. The
crucial issue is to explore more efficient and accurate
multidimensional mining of association patterns on
different granularities in a flexible and robust manner.
II. RELATED WORKS AND
TERMINOLOGIES
A. Frequent and Infrequent Rules
Records in a transactional database contain
simple items identified by Transaction IDs using
conventional methods. The notion of association is
applied to capture the co- occurrence of items in
transactions. There are two important factors for
association rules: support and confidence. Support
means how often the rule applies while confidence
refers to how often the rule is true. We are likely to
find association rules with high confidence and
support. Some data mining approaches allow users to
set minimum support/confidence as the threshold for
mining [6, 10]. Efficient algorithms for finding
infrequent rules are also in development.
B. Multidimensional Data Mining
Finding association rules involving various
attributes efficiently is an important subject for data
mining. Association Rule Clustering System (ARCS)
was proposed in [9], where association clustering is
proposed for a 2- dimensional space. The restriction of
ARCS is that only one rule is generated in each scan.
Hence, it takes massive redundant scans to find all
rules.
The method proposed in [9] mines all large
itemsets at first and then use a relationship graph to
assign attribute according to user given priorities of
each attribute. Since the method is meant to discover
large itemsets over a database as the whole, infrequent
RESEARCH ARTICLE OPEN ACCESS
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rules that hold in smaller granularities will be lost.
Different priorities of the condition attributes will
induce different rules so that user may need to try
with all possible priorities to discover all rules.
C. Apriori Algorithm
The Apriori algorithm is a level wise
iterative search algorithm for mining frequent itemsets
with regards to association rules [1,3-5,13]. The
weakness of Apriori algorithm s that it requires k
passes of database scans when the cardinality of the
longest frequent itemsets is k. the algorithm is also
computation intensive in generating the candidate
itemsets and computing of support values. If the
number of first itemsets element is k, the database will
be scanned k times at least. Hence it is not efficient
enough.
A variant of Apriori algorithm is the
AprioriTID [2]. The AprioriTID reduces the time
required for frequency counting by replacing every
transaction in the database by a set of candidate sets
that occurs in that transaction [2]. Although the
AprioriTID algorithm is much faster in later iterations,
it is much slower in early iterations as compared to the
original Apriori algorithm. Another drawback of
AprioriTID is inefficient use of space; the database
modified by Apriori- gen can be much larger than the
initial database.
D. Concept Description and Taxonomy
The issue of data structure and descriptive
model for mining is less discussed when comparing
works on algorithms. The concept description task is
problematic since the term concept description is used
in different ways. In this situation, researchers argue
for a de facto standard definition for the term [10, 11].
At this stage it is easier to deal with common criterion
on higher abstraction level for concept description
such as comprehension [11] and compatibility [6].
Han & Kamber view concept description as
a form of data generalization and define concept
description as a task that generates descriptions for the
characterization and comparison of the data [11].
Ontology provides a vocabulary for specific
domains and defines the meaning of the terms and
relationships between them in practical situations. The
term Taxonomy is used in this paper as it is more
flexible and can cover cases with no semantic
meaning.
II. THE MULTIDIMENSIONAL
MINING APPROACH
A. Representation Schema and Data Structure
Figure 1. Forest of Concept Taxonomy
For the sakes of comprehension and
compatibility, we use the forest structure consisting of
Concept-Taxonomies to represent the overall searching
space, i.e. the set of all propositions of the concepts.
On top of this structure, the sets of association patterns
can be formed by selecting concepts from individual
taxonomies. The notions can be clarified with
examples as follows:
1). Taxonomy: A category consists of domain
concepts in a latticed hierarchical structure, while
each member per se can be in turn a taxonomy.
An example for customer’s characteristics
can be [Age, Sex,
Occupation…], while the taxonomy of occupation
can be [manager, labor, teacher, engineer…].
2). Forest of concept taxonomies: A hyper-graph
for representing universe of discourse or the
closed-world of interests built with domain
taxonomies with regards to location and Sex of
customers is shown in Fig 1.
3). Association Rule: a pattern consisting of
elements taken from various concept taxonomies
such as
[(location=web),(Sex=female),(Goods=milk)].
By multidimensional data mining of
association rules, the notion of relation often refers to
the belonging relationship between elementary patterns
and generalized patterns rather than semantics [6].
Other notations will be used in this paper are shown in
Table 1.
TABLE I. CONCEPTS AND NOTATION
Notation Definition
TID Transaction identifier
MD Multidimensional database
CT Concept Taxonomy
Ei The i-th element segment
T[Ei] An element segment over Ei in MD
Gi The j-th generalized pattern
T[Gj] The j-th combined segment over Gj
REi Rules with regards to the i-th element
segmentRGj Rules with regards to the j-th generalized
pattern(Gj,r) Association rules over Gj with regards to to
match ratio rm Ratio for a relax match, given by the user.
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Mar Apr May Spring,
Male
Spring,
Female
Spring
Male 1 0 0 1 0 1
Female 1 0 0 0 1 1
Male 0 1 0 1 0 1
Female 0 1 0 0 1 1
Male 0 0 1 1 0 1
Female 0 0 1 0 1 1
B. The Data Mining Algorithm
Outline of the proposed algorithm is shown
in Fig 2. The input of the mining process involves 5
entities: 1) a multidimensional transaction database
MD (optional when a default MD is assigned), 2) a
set of concept taxonomies for each dimension (CT),
3) a minimal support viz. minSup, 4) a minimal
confidence, viz. minConf, and 5) a match ration m for
the relaxed match.
The output of the algorithm is the
multidimensional association with regards to a
full/relaxed match in the MD. The mining process can
be characterized in two independent steps: 1) finding
all itemsets in each element segment and 2) updating
all combinations of segments by using the output of
step 1. For practical reasons, step 1 of the algorithm
can be replaced by similar algorithms such as
Apriori. This segregation of the two steps enables
flexible mining and ease of use in distributed
environments.
1 Input:
2) Multidimensional Transaction Database MD
3 Concept taxonomies for each dimension: CTx(X=
1-n)
4) User given threshold: minSu,p, minConf, match
rat;io m
5 Procedure:
6 Phase0:
7) to generate all Ei and Gj by CTx (x = 1 to n);
8) build the pattern table;
9 Phase1:
10)For all Ei ⊂ G
11)to discover all association rules r in T[Ei] as REi
12)Phase2:
13)for all Ei
14)for all Gjthat Ei ⊂ Gj
15)to update RGj using REi;
16)Phase3:
17)for all Gj
18)For all r (which satisfy m) in RGj
19)output (Gj, r);
20)Output:
21)all multidimensional association rules(p, r)
Figure 2. Outline of algorithm
The task of the algorithm is to discover all association
rules REi in the element segment T[Ei] for each
element pattern Ei. It then uses REi to update
RGj, i.e the set of
association rules for every generalized pattern Gj
which covers Ei. The task done by each element
pattern is to find large itemsets in itself and
acknowledge its super generalized
patterns with the association rules. The task don by
each generalized pattern is o decide which rules hold
within it according to the acknowledgements from the
element patterns. The mining procedure needs only to
work on each element segment and uses the output
from each segment to determine which rules hold in
the combined segments. Thus, there is no need to scan
all of the possible segments for finding the rules.
C. Generating all patterns and the
pattern table
The procedure generates all elementary and
generalized patterns with the given forest, where a
pattern table for recording the belonging relationship
between the elementary and generalized patterns is
built. Given a set of concept taxonomies, a multi-
dimensional pattern can be generated by choosing a
node from each concept taxonomy. The combination
of different choices represents all the represents all
the multidimensional patterns. For example, Fig 3
presents a situation of 12 patterns.
Figure 3. Belonging relationships between patterns
TABLE II. THE PATTERN TABLE FOR
RELATIONS SHOWN IN FIG 3
Fig 3 also shows the belonging relationship between
patterns in a lattice structure. The relationships are
recorded in the form of bit map which includes
element patterns and generalized patterns. In the table,
a “1” indicates that the element pattern belongs to the
corresponding generalized pattern and “0” indicates
case vice versa. Table II shows a bit map which stores
such relationships.
D. Update Process
1) for all REi
2) for all Gj ⊃Ei
3) if (RGj never be updated)
4) RGj = REi;
5) else
6) RGj= RGj ∩ REi;
Figure 4. The “update” algorithm
After all patterns have been generated and the pattern
table has been built, the procedure begins to read the
transactions with regards to each element segment
to discover all association patterns. Besides our
algorithm, the Apriori algorithm can also be used in
this phase. The output of this phase is all of REi for
each element patter Ei. It will be fed as the input to
the next phase for updating each RGj using REi. Fig
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4 shows the outline of the update procedure with a full
match.
E. The output function
For a full match, the mining process outputs all
(Gj, r)
pairs for every r left in each RGj. For a relaxed
match, it outputs all (Gj,r) pair for every r in each
RGj where the count exceeds |mT[Gj]| by a relax
match. By means of algorithm described above, loss
of finding the rules that only hold in some segments
can be prevented and
pickup of multidimensional association
rules that do not holder over all the range of the
domain can be avoided.
IV. THE EXPERIMENT AND
EVALUATION
A. Experiment scenario of a wholesale case
To measure and prove the performance of the
method, a scenario for a wholesales business using
synthetic data are established for the test. The
wholesales enterprise has various business branches
and a web-site for its business operations.
Data from four branches and the website are
gathered for the experiment. We take five of the
various attributes (Abode, Sex, Occupation, Age and
Marriage) as the dimensions for the test. Adding with
the product catalog and price/profit record, there are 7
dimensions and we build the concept taxonomies for
each dimension.
To examine the effect of different customer
behaviors, we generate three data types as illustrated
in Table III. The parameters and the default values of
the data sets are shown in Table IV. There are 118
multidimensional patterns from these taxonomies, 44
of them are element patterns and the other 74 of
them are generalized ones. The mining tool should
find all large itemsets for the 74 generalized patterns.
TABLE III. THREE TYPES OF DATA SETS
Type 1 To generate a single set of maximal potentially
large itemsets
and then generate transactions for each element
pattern Ei following apriori-gen.[4]
Type 2 Besides a set of common maximal potential large
itemsets, to
genreate maximal potentially large itemsets for
each element pattern Ei, and then genreate
transactions for each element pattern Ei. The
common maximal potentially large itemsets
respectively following the apriori-gen[4].
Type 3 Generating a set of maximal potentially large
itemsets for each
element pattern Ei, and generating transactions for
each element pattern Ei from its own maximal
potentially large items following the apriori-gen.[4]
TABLE IV. PARAMETERS AND DEFAULT
VALUES OF DATA SETS
Notation Meaning Default
|D| Number of transactions 100K
|T| Average size of transactions 6
|I| Average size of maximal potentially large
itemsets
4
|L| NBumber of maximal potentially large
itemsets
1000
N Number of items 1000
SM The maximum size of segmentation 50
B. Experiment Results
At first, the 74 generalized patterns are
successfully found. The key feature of the algorithm
as shown in Fig. 5 is that it is linear (and hence highly
scalable) to the number of records and that it is
flexible in terms of reading various data types. The
test result w.r.t scalability in Fig. 5 shows that the
algorithm takes execution time linear to the number
of transactions of all three data types. The experiment
results of both the test (see Figure 5 and 6) shows that
our algorithm is superior to conventional methods in
several areas:
Figure 5. Execution Time w.r.t to the no. of
transactions
Figure 6. Scalability test w.r.t. the no. of records
Execution time with regards to number of
transactions is linear for the data types tested for the
whole process. This means that the time and space
cost of executing our algorithm do not increase
exponentially as compared to conventional methods.
Phase 2 ( the update phase ) of our algorithm
is an important space and time saver as shown by the
Figure 8; execution time is also linear and time
taken to read up to
2000k records took less than 5 seconds. This means
that data patterns from new data can be quickly
extracted and used to update the existing pattern table
for immediate use. result in Fig 5 shows that the
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algorithm takes linear time to the number of
transactions.
Figure 7. Efficiency in relation to the number of
element patterns
In general, an increase of element patterns with
result in an increase in execution time; the key to
scalability is having the execution time increasing in a
linear manner with an increase in element patterns. In
Figure 7, all three data types experienced an increase
of execution time with an increase of element pattern
in a linear fashion, thus making our algorithm
efficient.
Most importantly, an increase in element patterns
leads to a less than proportion increase in execution
time, making out the algorithm highly scalable.
Reading off Figure 7, a 4 time increase of 30 element
patterns from 10 to 40 will result in:
1) 75 times increase in execution time for data
type 1 from 20 seconds to 35 seconds
2) 67 times increase in execution time for data
type 2 from 15 seconds to 25 seconds.
3) 05 times increase in execution time for data
type 3
from approximately 22 seconds to 45 seconds.
The test results shows that for increasing the
number of element patterns will not decrease the
efficiency of the algorithm. It fulfills the requirement
of scalability in terms of number of element patterns
as well.
After we have understood the execution
effectiveness and flexibility of the algorithm, the
next step is evaluate the impact of various user
inputs on the algorithm. As described earlier in this
paper, the main user inputs are minimum support
minSup, minimum confidence minConf, and match
ratio m. The impact of user input on the algorithm is
shown in Figure 8, 9, 10 and 11 respectively.
C. Impact of User Input on the Algorithm
The impact of minSup on the algorithm
can be categorized in terms of efficiency, discrete
ratio and lost ratio. All of such algorithms are
sensitive to the minimum support; the smaller the
minimum support, the longer the execution time.
However, we have shown that the real execution
time of the step 2 (the update) in the proposed
algorithm is relatively much shorter than the whole
process (see Fig. 5).
The test results proved that an increase in minSup
will lead to greater returns of investment in terms of
time efficiency; this is in line with one of the core
objectives of building an efficient algorithm. Our
algorithm is more efficient than conventional methods
in terms of execution time over data. For instance in
Figure 8, a 10 time increase (from 0.1 to 1) in minSup
leads to a more than proportionate decrease in
execution time across all data types:
1) Execution time for data type 1 decreased by
approximately 10 times, from approximately 400
seconds to approximately 40 seconds in terms of
execution time.
2) Execution time for data type 2 decreased
by more than 30 times, from more
than 600 seconds to approximately 20
seconds in terms of execution time.
3) Execution time for data type 3 decreased by
more
than 11 times, from approximately 350 seconds to
approximately 30 seconds in terms of execution time.
The test results proved that an increase in minSup
will lead to greater returns of investment in terms of
time efficiency; this is in line with one of the core
objectives of building an efficient algorithm.
Figure 8. Efficiency in Relation to Minimum
Support
The discrete ratio is the ratio of the number
of rules pruned by the improved algorithm to the
number of rules discovered by prior mining
approaches. Figure 10 shows the ratio of rules pruned
by the improved algorithm against minSup.
In general, all three data types (except for data
type 1) exhibited an increase of ratio with an
increase of minSup from approximately 0.2% to 2%.
The test results point the fact that the proposed
algorithm can effectively decrease unwanted
generalized patterns in which elemental data patterns
is not true. This greatly helps users to focus on data
patterns that are useful for their organizations while
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uncovering niche data patterns. For instance with a
higher setting value, only <Female, Age over
60, take AP transfusions> will be found instead of
<Age over
60, take AP transfusions>.
Figure 9. Effects of minSup on Discrete Large
Itemsets Ratio
Figure 10 shows the test result on lost ratio, i.e.
the influence of minSup values on the lost rules by
other mining tools in comparison to this approach. All
three data types experienced an increase in lost ratio
over an increase in minSup from 0.25% to 2%, with
the greatest increase in data type 2, followed by data
type 3 and finally data type 1.
The test results prove that the proposed algorithm will
help users uncover useful data patterns which
otherwise would be uncovered by traditional
approaches. Thus, our objective of uncovering niche
data patterns that would otherwise be left out is met
and proved by this test result.
Figure 10. Effects of minSup on Lost Itemsets
Figure 11. Effects of match ratio on discrete large
itemsets ratio
Increasing the match ratio would reduce unwanted
data patterns in general. Figure 11 shows the effect of
match ratio (r) on discrete ratio.
Similar to the above test results, an increase of
m from 0.5 to 1 results in a more than proportional
increase in discrete ratio across all three forms of data
types. The significance of this test result is
congruent with the test results above; the algorithm
is efficient and scalable without losing flexibility and
helps uncover niche data patterns.
V.SUMMARY
The article proposes an approach which includes a
novel data structure and an efficient algorithm for
mining association rules on various granularities. The
advantages of this approach over existing ones
include 1) greater comprehensiveness and easy of use.
2) More efficient with limited scans and storage I/O
operations. 3) More effective in terms of finding rules
that hold in different granularity levels.
4) Association patterns can be stored so that
incremental mining is possible. 5) Information loss
rate is very low.
Thewhole process of the development
and experimental measurement of the multi
dimensional data mining approach was discussed in
this paper. The test result shows that its performance,
efficiency, scalability and information loss rate are
better than the current approaches. The effects of
perceived issues and potential development of data
mining and concept description are worthy of further
investigation.
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