This document provides a literature review on data mining with Oracle 10g using clustering and classification algorithms. It discusses the data mining process and common algorithms used, including Naive Bayes, decision trees, k-means clustering, and neural networks. The review categorizes data mining techniques into supervised learning (classification, prediction) and unsupervised learning (clustering, association rule mining). It also outlines the typical 4-step data mining process of problem definition, data preparation, model building and evaluation, and knowledge deployment.
CONFIGURING ASSOCIATIONS TO INCREASE TRUST IN PRODUCT PURCHASEIJwest
Clustering is categorizing data into groups with similar objects. Data mining adds to complexities of clustering a large dataset with various features. Among these datasets, there are electronic business stores which offer their products through web. These stores require recommendation systems which can offer products to the user which the user might require them with higher probability. In this study, previous purchases of users are used to present a sorted list of products to the user. Identifying associations related to users and finding centers increases precision of the recommended list. Configuration of associations and creating a profile for users is important in current studies. In the proposed method, association rules are presented to model user interactions in the web which use time that a page is visited and frequency of visiting a page to weight pages and describes users’ interest to page groups. Therefore, weight of each transaction item describes user’s interest in that item. Analyzing results show that the proposed method presents a more complete model of users’ behavior because it combines weight and membership degree of pages simultaneously for ranking candidate pages. This method has obtained higher accuracy compared to other methods even in higher number of pages.
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.
The International Journal of Engineering and Sciencetheijes
This document summarizes a research paper on discovering actionable knowledge through multi-step data mining. The paper proposes a framework that combines multiple data sources, mining methods, and features to generate comprehensive patterns. This approach aims to provide more reliable and dependable intelligence than single-step mining. The framework integrates multi-source, multi-method, and multi-feature combined mining techniques. A prototype application demonstrated the effectiveness of the proposed combined mining approach for generating actionable knowledge from complex enterprise data.
This document summarizes an article that proposes a new algorithm for efficiently mining both positive and negative association rules from transactional databases. The algorithm first constructs a frequent pattern tree (FP-tree) to store the transaction information. It then uses an FP-growth approach to iteratively find frequent patterns and generate the positive and negative association rules without candidate generation. The algorithm aims to overcome limitations of previous methods and efficiently find all valid comparative association rules.
Introduction to feature subset selection methodIJSRD
Data Mining is a computational progression to ascertain patterns in hefty data sets. It has various important techniques and one of them is Classification which is receiving great attention recently in the database community. Classification technique can solve several problems in different fields like medicine, industry, business, science. PSO is based on social behaviour for optimization problem. Feature Selection (FS) is a solution that involves finding a subset of prominent features to improve predictive accuracy and to remove the redundant features. Rough Set Theory (RST) is a mathematical tool which deals with the uncertainty and vagueness of the decision systems.
A SURVEY ON DATA MINING IN STEEL INDUSTRIESIJCSES Journal
In Industrial environments, huge amount of data is being generated which in turn collected indatabase anddata warehouses from all involved areas such as planning, process design, materials, assembly, production, quality, process control, scheduling, fault detection,shutdown, customer relation management, and so on. Data Mining has become auseful tool for knowledge acquisition for industrial process of Iron and steel making. Due to the rapid growth in Data Mining, various industries started using data mining technology to search the hidden patterns, which might further be used to the system with the new knowledge which might design new models to enhance the production quality, productivity optimum cost and maintenance etc. The continuous improvement of all steel production process regarding the avoidance of quality deficiencies and the related improvement of production yield is an essential task of steel producer. Therefore, zero defect strategy is popular today and to maintain it several quality assurancetechniques areused. The present report explains the methods of data mining and describes its application in the industrial environment and especially, in the steel industry.
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.
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.
CONFIGURING ASSOCIATIONS TO INCREASE TRUST IN PRODUCT PURCHASEIJwest
Clustering is categorizing data into groups with similar objects. Data mining adds to complexities of clustering a large dataset with various features. Among these datasets, there are electronic business stores which offer their products through web. These stores require recommendation systems which can offer products to the user which the user might require them with higher probability. In this study, previous purchases of users are used to present a sorted list of products to the user. Identifying associations related to users and finding centers increases precision of the recommended list. Configuration of associations and creating a profile for users is important in current studies. In the proposed method, association rules are presented to model user interactions in the web which use time that a page is visited and frequency of visiting a page to weight pages and describes users’ interest to page groups. Therefore, weight of each transaction item describes user’s interest in that item. Analyzing results show that the proposed method presents a more complete model of users’ behavior because it combines weight and membership degree of pages simultaneously for ranking candidate pages. This method has obtained higher accuracy compared to other methods even in higher number of pages.
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.
The International Journal of Engineering and Sciencetheijes
This document summarizes a research paper on discovering actionable knowledge through multi-step data mining. The paper proposes a framework that combines multiple data sources, mining methods, and features to generate comprehensive patterns. This approach aims to provide more reliable and dependable intelligence than single-step mining. The framework integrates multi-source, multi-method, and multi-feature combined mining techniques. A prototype application demonstrated the effectiveness of the proposed combined mining approach for generating actionable knowledge from complex enterprise data.
This document summarizes an article that proposes a new algorithm for efficiently mining both positive and negative association rules from transactional databases. The algorithm first constructs a frequent pattern tree (FP-tree) to store the transaction information. It then uses an FP-growth approach to iteratively find frequent patterns and generate the positive and negative association rules without candidate generation. The algorithm aims to overcome limitations of previous methods and efficiently find all valid comparative association rules.
Introduction to feature subset selection methodIJSRD
Data Mining is a computational progression to ascertain patterns in hefty data sets. It has various important techniques and one of them is Classification which is receiving great attention recently in the database community. Classification technique can solve several problems in different fields like medicine, industry, business, science. PSO is based on social behaviour for optimization problem. Feature Selection (FS) is a solution that involves finding a subset of prominent features to improve predictive accuracy and to remove the redundant features. Rough Set Theory (RST) is a mathematical tool which deals with the uncertainty and vagueness of the decision systems.
A SURVEY ON DATA MINING IN STEEL INDUSTRIESIJCSES Journal
In Industrial environments, huge amount of data is being generated which in turn collected indatabase anddata warehouses from all involved areas such as planning, process design, materials, assembly, production, quality, process control, scheduling, fault detection,shutdown, customer relation management, and so on. Data Mining has become auseful tool for knowledge acquisition for industrial process of Iron and steel making. Due to the rapid growth in Data Mining, various industries started using data mining technology to search the hidden patterns, which might further be used to the system with the new knowledge which might design new models to enhance the production quality, productivity optimum cost and maintenance etc. The continuous improvement of all steel production process regarding the avoidance of quality deficiencies and the related improvement of production yield is an essential task of steel producer. Therefore, zero defect strategy is popular today and to maintain it several quality assurancetechniques areused. The present report explains the methods of data mining and describes its application in the industrial environment and especially, in the steel industry.
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.
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.
This document reviews the use of data mining and neural network techniques for stock market prediction. It discusses how data mining can extract hidden patterns from large datasets and neural networks can handle nonlinear and uncertain financial data. Specifically, it examines how a combination of data mining and neural networks may improve the reliability of stock predictions by leveraging their complementary strengths. The document also provides an overview of common data mining and neural network methods used for this purpose, such as statistical data mining, neural network-based data processing, clustering, and fuzzy logic. It reviews several previous studies that found neural networks and other nonlinear techniques often outperform traditional statistical models at predicting stock prices and indices.
Classification on multi label dataset using rule mining techniqueeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
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.
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.
This document discusses various privacy preservation techniques in data mining. It summarizes classification, clustering, and association rule learning as common privacy preservation approaches. For classification, it describes decision trees, k-nearest neighbors, artificial neural networks, support vector machines, and naive Bayes models. It provides advantages and disadvantages of these techniques. The document concludes that privacy preservation techniques have emerged to allow for efficient and effective data mining while protecting sensitive data.
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.
A Trinity Construction for Web Extraction Using Efficient AlgorithmIOSR Journals
This document describes a proposed system for web extraction using a trinity construction and efficient algorithms. It begins with an abstract discussing how trinity characteristics can be used to automatically extract content from websites in a sequential tree structure. It then discusses the existing system which uses trinity tree and prefix/suffix sorting but has limitations. The proposed system introduces fuzzy logic for multi-perspective crawling across multiple websites. A genetic algorithm is used to load extracted content into the trinity structure and remove unwanted data. Finally, an ant colony optimization algorithm is used to obtain an effective structure and suggest optimized solutions.
EXPLORING DATA MINING TECHNIQUES AND ITS APPLICATIONSeditorijettcs
This document discusses various data mining techniques. It begins with an introduction to data mining, explaining that it is used to discover patterns in large datasets. It then describes five major techniques: association, which finds relationships between items purchased together; classification, which assigns items to predefined categories; clustering, which automatically groups similar objects; prediction, which discovers relationships to predict future outcomes; and sequential patterns, which finds patterns over time. The document concludes by discussing some applications of data mining such as customer profiling, website analysis, and fraud detection.
A statistical data fusion technique in virtual data integration environmentIJDKP
Data fusion in the virtual data integration environment starts after detecting and clustering duplicated
records from the different integrated data sources. It refers to the process of selecting or fusing attribute
values from the clustered duplicates into a single record representing the real world object. In this paper, a
statistical technique for data fusion is introduced based on some probabilistic scores from both data
sources and clustered duplicates
TUPLE VALUE BASED MULTIPLICATIVE DATA PERTURBATION APPROACH TO PRESERVE PRIVA...IJDKP
Huge volume of data from domain specific applications such as medical, financial, library, telephone,
shopping records and individual are regularly generated. Sharing of these data is proved to be beneficial
for data mining application. On one hand such data is an important asset to business decision making by
analyzing it. On the other hand data privacy concerns may prevent data owners from sharing information
for data analysis. In order to share data while preserving privacy, data owner must come up with a solution
which achieves the dual goal of privacy preservation as well as an accuracy of data mining task –
clustering and classification. An efficient and effective approach has been proposed that aims to protect
privacy of sensitive information and obtaining data clustering with minimum information loss
Recommendation system using bloom filter in mapreduceIJDKP
Many clients like to use the Web to discover product details in the form of online reviews. The reviews are
provided by other clients and specialists. Recommender systems provide an important response to the
information overload problem as it presents users more practical and personalized information facilities.
Collaborative filtering methods are vital component in recommender systems as they generate high-quality
recommendations by influencing the likings of society of similar users. The collaborative filtering method
has assumption that people having same tastes choose the same items. The conventional collaborative
filtering system has drawbacks as sparse data problem & lack of scalability. A new recommender system is
required to deal with the sparse data problem & produce high quality recommendations in large scale
mobile environment. MapReduce is a programming model which is widely used for large-scale data
analysis. The described algorithm of recommendation mechanism for mobile commerce is user based
collaborative filtering using MapReduce which reduces scalability problem in conventional CF system.
One of the essential operations for the data analysis is join operation. But MapReduce is not very
competent to execute the join operation as it always uses all records in the datasets where only small
fraction of datasets are applicable for the join operation. This problem can be reduced by applying
bloomjoin algorithm. The bloom filters are constructed and used to filter out redundant intermediate
records. The proposed algorithm using bloom filter will reduce the number of intermediate results and will
improve the join performance.
The document discusses a link mining methodology adapted from the CRISP-DM process to incorporate anomaly detection using mutual information. It applies this methodology in a case study of co-citation data. The methodology involves data description, preprocessing, transformation, exploration, modeling, and evaluation. Hierarchical clustering identified 5 clusters, with cluster 1 showing strong links and cluster 5 weak links. Mutual information validated the results, showing cluster 5 had the lowest mutual information, indicating independent variables. The case study demonstrated the approach can interpret anomalies semantically and be used with real-world data volumes and inconsistencies.
Additional themes of data mining for Msc CSThanveen
Data mining involves using computational techniques from machine learning, statistics, and database systems to discover patterns in large data sets. There are several theoretical foundations of data mining including data reduction, data compression, pattern discovery, probability theory, and inductive databases. Statistical techniques like regression, generalized linear models, analysis of variance, and time series analysis are also used for statistical data mining. Visual data mining integrates data visualization techniques with data mining to discover implicit knowledge. Audio data mining uses audio signals to represent data mining patterns and results. Collaborative filtering is commonly used for product recommendations based on opinions of other customers. Privacy and security of personal data are important social concerns of data mining.
PERFORMING DATA MINING IN (SRMS) THROUGH VERTICAL APPROACH WITH ASSOCIATION R...Editor IJMTER
This system technique is used for efficient data mining in SRMS (Student Records
Management System) through vertical approach with association rules in distributed databases. The
current leading technique is that of Kantarcioglu and Clifton[1]. In this system I deal with two
challenges or issues, one that computes the union of private subsets that each of the interacting users
hold, and another that tests the inclusion of an element held by one user in a subset held by another.
The existing system uses different techniques for data mining purpose like Apriori algorithm. The
Fast Distributed Mining (FDM) algorithm of Cheung et al. [2], which is an unsecured distributed
version of the Apriori algorithm. Proposed system offers enhanced privacy and data mining with
respect to the Encryption techniques and Association rule with Fp-Growth Algorithm in private
cloud (system contains different files of subjects with respect to their branches). Due to this above
techniques the expected effect on this system is that, it is simpler and more efficient in terms of
communication cost and combinational cost. Due to these techniques it will affect the parameter like
time consumption for execution, length of the code is decrease, find the data fast, extracting hidden
predictive information from large databases and the efficiency of this proposed system should
increase by the 20%.
CLUSTERING ALGORITHM FOR A HEALTHCARE DATASET USING SILHOUETTE SCORE VALUEijcsit
The huge amount of healthcare data, coupled with the need for data analysis tools has made data mining
interesting research areas. Data mining tools and techniques help to discover and understand hidden
patterns in a dataset which may not be possible by mainly visualization of the data. Selecting appropriate
clustering method and optimal number of clusters in healthcare data can be confusing and difficult most
times. Presently, a large number of clustering algorithms are available for clustering healthcare data, but
it is very difficult for people with little knowledge of data mining to choose suitable clustering algorithms.
This paper aims to analyze clustering techniques using healthcare dataset, in order to determine suitable
algorithms which can bring the optimized group clusters. Performances of two clustering algorithms (Kmeans
and DBSCAN) were compared using Silhouette score values. Firstly, we analyzed K-means
algorithm using different number of clusters (K) and different distance metrics. Secondly, we analyzed
DBSCAN algorithm using different minimum number of points required to form a cluster (minPts) and
different distance metrics. The experimental result indicates that both K-means and DBSCAN algorithms
have strong intra-cluster cohesion and inter-cluster separation. Based on the analysis, K-means algorithm
performed better compare to DBSCAN algorithm in terms of clustering accuracy and execution time.
Ideas2Market webinar - Is your idea feasibleSteve Bryant
The document provides 15 questions to ask oneself to validate and assess the feasibility of a new idea, covering areas such as intellectual property research, confidentiality, regulations, market research, competition, barriers to entry, funding, and assembling a team. It emphasizes the importance of conducting thorough research on these topics before pursuing an idea to avoid wasted time and money. Answering the questions can help one determine if an idea is worth pursuing or if it is not viable.
The document outlines the steps to prepare a garden including preparing the land, building raised beds, crushing cow manure as fertilizer, filling and planting the beds, weeding, and eventually seeing the fruits of their labor from the garden crew's work.
La persona desea que el destinatario sea su sueño del que nunca quiere despertar. Ve su mundo a su manera junto a esa persona. Le dice cosas bonitas como "chiquihermosa" y "pelodivino".
Una persona invita a otra a su casa para pasar un buen rato. La otra persona acepta la invitación y van a la casa. Al poco tiempo empiezan actividades sexuales explícitas de forma entusiasta. Sin embargo, la situación se vuelve negativa y agresiva, terminando con una de las personas echada de la casa.
Bill Gates was born in Seattle, Washington in 1955. He is now one of the richest people in the world with a net worth of $65 billion due to co-founding the software company Microsoft. Gates lives in Medina, Washington with his wife Melinda and their three children.
This document reviews the use of data mining and neural network techniques for stock market prediction. It discusses how data mining can extract hidden patterns from large datasets and neural networks can handle nonlinear and uncertain financial data. Specifically, it examines how a combination of data mining and neural networks may improve the reliability of stock predictions by leveraging their complementary strengths. The document also provides an overview of common data mining and neural network methods used for this purpose, such as statistical data mining, neural network-based data processing, clustering, and fuzzy logic. It reviews several previous studies that found neural networks and other nonlinear techniques often outperform traditional statistical models at predicting stock prices and indices.
Classification on multi label dataset using rule mining techniqueeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
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.
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.
This document discusses various privacy preservation techniques in data mining. It summarizes classification, clustering, and association rule learning as common privacy preservation approaches. For classification, it describes decision trees, k-nearest neighbors, artificial neural networks, support vector machines, and naive Bayes models. It provides advantages and disadvantages of these techniques. The document concludes that privacy preservation techniques have emerged to allow for efficient and effective data mining while protecting sensitive data.
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.
A Trinity Construction for Web Extraction Using Efficient AlgorithmIOSR Journals
This document describes a proposed system for web extraction using a trinity construction and efficient algorithms. It begins with an abstract discussing how trinity characteristics can be used to automatically extract content from websites in a sequential tree structure. It then discusses the existing system which uses trinity tree and prefix/suffix sorting but has limitations. The proposed system introduces fuzzy logic for multi-perspective crawling across multiple websites. A genetic algorithm is used to load extracted content into the trinity structure and remove unwanted data. Finally, an ant colony optimization algorithm is used to obtain an effective structure and suggest optimized solutions.
EXPLORING DATA MINING TECHNIQUES AND ITS APPLICATIONSeditorijettcs
This document discusses various data mining techniques. It begins with an introduction to data mining, explaining that it is used to discover patterns in large datasets. It then describes five major techniques: association, which finds relationships between items purchased together; classification, which assigns items to predefined categories; clustering, which automatically groups similar objects; prediction, which discovers relationships to predict future outcomes; and sequential patterns, which finds patterns over time. The document concludes by discussing some applications of data mining such as customer profiling, website analysis, and fraud detection.
A statistical data fusion technique in virtual data integration environmentIJDKP
Data fusion in the virtual data integration environment starts after detecting and clustering duplicated
records from the different integrated data sources. It refers to the process of selecting or fusing attribute
values from the clustered duplicates into a single record representing the real world object. In this paper, a
statistical technique for data fusion is introduced based on some probabilistic scores from both data
sources and clustered duplicates
TUPLE VALUE BASED MULTIPLICATIVE DATA PERTURBATION APPROACH TO PRESERVE PRIVA...IJDKP
Huge volume of data from domain specific applications such as medical, financial, library, telephone,
shopping records and individual are regularly generated. Sharing of these data is proved to be beneficial
for data mining application. On one hand such data is an important asset to business decision making by
analyzing it. On the other hand data privacy concerns may prevent data owners from sharing information
for data analysis. In order to share data while preserving privacy, data owner must come up with a solution
which achieves the dual goal of privacy preservation as well as an accuracy of data mining task –
clustering and classification. An efficient and effective approach has been proposed that aims to protect
privacy of sensitive information and obtaining data clustering with minimum information loss
Recommendation system using bloom filter in mapreduceIJDKP
Many clients like to use the Web to discover product details in the form of online reviews. The reviews are
provided by other clients and specialists. Recommender systems provide an important response to the
information overload problem as it presents users more practical and personalized information facilities.
Collaborative filtering methods are vital component in recommender systems as they generate high-quality
recommendations by influencing the likings of society of similar users. The collaborative filtering method
has assumption that people having same tastes choose the same items. The conventional collaborative
filtering system has drawbacks as sparse data problem & lack of scalability. A new recommender system is
required to deal with the sparse data problem & produce high quality recommendations in large scale
mobile environment. MapReduce is a programming model which is widely used for large-scale data
analysis. The described algorithm of recommendation mechanism for mobile commerce is user based
collaborative filtering using MapReduce which reduces scalability problem in conventional CF system.
One of the essential operations for the data analysis is join operation. But MapReduce is not very
competent to execute the join operation as it always uses all records in the datasets where only small
fraction of datasets are applicable for the join operation. This problem can be reduced by applying
bloomjoin algorithm. The bloom filters are constructed and used to filter out redundant intermediate
records. The proposed algorithm using bloom filter will reduce the number of intermediate results and will
improve the join performance.
The document discusses a link mining methodology adapted from the CRISP-DM process to incorporate anomaly detection using mutual information. It applies this methodology in a case study of co-citation data. The methodology involves data description, preprocessing, transformation, exploration, modeling, and evaluation. Hierarchical clustering identified 5 clusters, with cluster 1 showing strong links and cluster 5 weak links. Mutual information validated the results, showing cluster 5 had the lowest mutual information, indicating independent variables. The case study demonstrated the approach can interpret anomalies semantically and be used with real-world data volumes and inconsistencies.
Additional themes of data mining for Msc CSThanveen
Data mining involves using computational techniques from machine learning, statistics, and database systems to discover patterns in large data sets. There are several theoretical foundations of data mining including data reduction, data compression, pattern discovery, probability theory, and inductive databases. Statistical techniques like regression, generalized linear models, analysis of variance, and time series analysis are also used for statistical data mining. Visual data mining integrates data visualization techniques with data mining to discover implicit knowledge. Audio data mining uses audio signals to represent data mining patterns and results. Collaborative filtering is commonly used for product recommendations based on opinions of other customers. Privacy and security of personal data are important social concerns of data mining.
PERFORMING DATA MINING IN (SRMS) THROUGH VERTICAL APPROACH WITH ASSOCIATION R...Editor IJMTER
This system technique is used for efficient data mining in SRMS (Student Records
Management System) through vertical approach with association rules in distributed databases. The
current leading technique is that of Kantarcioglu and Clifton[1]. In this system I deal with two
challenges or issues, one that computes the union of private subsets that each of the interacting users
hold, and another that tests the inclusion of an element held by one user in a subset held by another.
The existing system uses different techniques for data mining purpose like Apriori algorithm. The
Fast Distributed Mining (FDM) algorithm of Cheung et al. [2], which is an unsecured distributed
version of the Apriori algorithm. Proposed system offers enhanced privacy and data mining with
respect to the Encryption techniques and Association rule with Fp-Growth Algorithm in private
cloud (system contains different files of subjects with respect to their branches). Due to this above
techniques the expected effect on this system is that, it is simpler and more efficient in terms of
communication cost and combinational cost. Due to these techniques it will affect the parameter like
time consumption for execution, length of the code is decrease, find the data fast, extracting hidden
predictive information from large databases and the efficiency of this proposed system should
increase by the 20%.
CLUSTERING ALGORITHM FOR A HEALTHCARE DATASET USING SILHOUETTE SCORE VALUEijcsit
The huge amount of healthcare data, coupled with the need for data analysis tools has made data mining
interesting research areas. Data mining tools and techniques help to discover and understand hidden
patterns in a dataset which may not be possible by mainly visualization of the data. Selecting appropriate
clustering method and optimal number of clusters in healthcare data can be confusing and difficult most
times. Presently, a large number of clustering algorithms are available for clustering healthcare data, but
it is very difficult for people with little knowledge of data mining to choose suitable clustering algorithms.
This paper aims to analyze clustering techniques using healthcare dataset, in order to determine suitable
algorithms which can bring the optimized group clusters. Performances of two clustering algorithms (Kmeans
and DBSCAN) were compared using Silhouette score values. Firstly, we analyzed K-means
algorithm using different number of clusters (K) and different distance metrics. Secondly, we analyzed
DBSCAN algorithm using different minimum number of points required to form a cluster (minPts) and
different distance metrics. The experimental result indicates that both K-means and DBSCAN algorithms
have strong intra-cluster cohesion and inter-cluster separation. Based on the analysis, K-means algorithm
performed better compare to DBSCAN algorithm in terms of clustering accuracy and execution time.
Ideas2Market webinar - Is your idea feasibleSteve Bryant
The document provides 15 questions to ask oneself to validate and assess the feasibility of a new idea, covering areas such as intellectual property research, confidentiality, regulations, market research, competition, barriers to entry, funding, and assembling a team. It emphasizes the importance of conducting thorough research on these topics before pursuing an idea to avoid wasted time and money. Answering the questions can help one determine if an idea is worth pursuing or if it is not viable.
The document outlines the steps to prepare a garden including preparing the land, building raised beds, crushing cow manure as fertilizer, filling and planting the beds, weeding, and eventually seeing the fruits of their labor from the garden crew's work.
La persona desea que el destinatario sea su sueño del que nunca quiere despertar. Ve su mundo a su manera junto a esa persona. Le dice cosas bonitas como "chiquihermosa" y "pelodivino".
Una persona invita a otra a su casa para pasar un buen rato. La otra persona acepta la invitación y van a la casa. Al poco tiempo empiezan actividades sexuales explícitas de forma entusiasta. Sin embargo, la situación se vuelve negativa y agresiva, terminando con una de las personas echada de la casa.
Bill Gates was born in Seattle, Washington in 1955. He is now one of the richest people in the world with a net worth of $65 billion due to co-founding the software company Microsoft. Gates lives in Medina, Washington with his wife Melinda and their three children.
The document discusses the history and development of artificial intelligence over the past several decades. Early AI research focused on symbolic approaches using logic and rules to represent knowledge. More recently, machine learning techniques such as deep learning have proven very successful, especially for perception and language tasks. However, fully general human-level artificial intelligence remains an ongoing challenge.
This document describes using MATLAB to analyze a synthetic time series dataset representing climate data over 500,000 years. The time series contains periodic signals at 100ky, 41ky and 21ky. Random noise and a long term trend are added. Fourier analysis is used to identify the dominant periodic components in the frequency domain. A Hamming window and bandpass filter are applied to further analyze specific frequency bands like the 21ky signal. Autocorrelation is also examined to identify cyclic patterns in the time series.
This method statement summarizes the pipe welding work to be done at a power generation building. It outlines the key equipment needed like welding machines, materials, and safety gear. It describes access to the work area, fall protection measures, and hazardous substances. The responsibilities of roles involved are defined. The work sequence is then outlined which involves preparing pipes and fittings, fitting up as per welding procedures, qualified supervision, and quality inspection. Installation and inspection will follow the quality control document plan.
Perubahan anatomi dan adaptasi fisiologi ibu hamilbintangzwitsal28
Perubahan anatomi dan adaptasi fisiologi pada ibu hamil terjadi pada sistem reproduksi dan payudara. Sistem reproduksi seperti uterus, vagina, ovarium, dan serviks mengalami perubahan untuk menyesuaikan kehamilan. Uterus membesar, vagina melebar, ovarium berhenti ovulasi, dan serviks lunak. Pada payudara, ukuran dan warna areola bertambah besar untuk mempersiapkan laktasi.
This document reviews the use of data mining and neural network techniques for stock market prediction. It discusses how data mining can extract hidden patterns from large datasets and make predictions about future trends. Neural networks are also effective for stock prediction due to their ability to handle uncertain and changing data. The document examines different data mining methods like statistical analysis, neural networks, clustering and fuzzy sets. It suggests that combining data mining and neural networks could improve the reliability of stock market predictions by uncovering the nonlinear patterns in stock price data.
Indexing based Genetic Programming Approach to Record Deduplicationidescitation
In this paper, we present a genetic programming (GP) approach to record
deduplication with indexing techniques.Data de-duplication is a process in which data are
cleaned from duplicate records due to misspelling, field swap or any other mistake or data
inconsistency. This process requires that we identify objects that are included in more than
one list.The problem of detecting and eliminating duplicated data is one of the major
problems in the broad area of data cleaning and data quality in data warehouse. So, we
need to create such a algorithm that can detect and eliminate maximum duplications.GP
with indexing is one of the optimization technique that helps to find maximum duplicates in
the database. We used adeduplication function that is able to identify whether two or more
entries in a repository are replicas or not. As many industries and systems depend on the
accuracy and reliability of databases to carry out operations. Therefore, the quality of the
information stored in the databases, can have significant cost implications to a system that
relies on information to function and conduct business. Moreover, this is fact that clean and
replica-free repositories not only allow the retrieval of higher quality information but also
lead to more concise data and to potential savings in computational time and resources to
process this data.
Index
Data mining involves analyzing large amounts of data to discover patterns that can be used for purposes such as increasing sales, reducing costs, or detecting fraud. It allows companies to better understand customer behavior and develop more effective marketing strategies. Common data mining techniques used by retailers include loyalty programs to track purchasing patterns and target customers with personalized coupons. Data mining software uses techniques like classification, clustering, and prediction to analyze data from different perspectives and extract useful information and patterns.
Configuring Associations to Increase Trust in Product Purchase dannyijwest
Clustering is categorizing data into groups with similar objects. Data mining adds to complexities of clustering a large dataset with various features. Among these datasets, there are electronic business stores which offer their products through web. These stores require recommendation systems which can offer products to the user which the user might require them with higher probability. In this study, previous purchases of users are used to present a sorted list of products to the user. Identifying associations related to users and finding centers increases precision of the recommended list. Configuration of associations and creating a profile for users is important in current studies. In the proposed method, association rules are presented to model user interactions in the web which use time that a page is visited and frequency of visiting a page to weight pages and describes users’ interest to page groups. Therefore, weight of each transaction item describes user’s interest in that item. Analyzing results show that the proposed method presents a more complete model of users’ behavior because it combines weight and membership degree of pages simultaneously for ranking candidate pages. This method has obtained higher accuracy compared to other methods even in higher number of pages.
Study of Data Mining Methods and its ApplicationsIRJET Journal
This document discusses data mining methods and their applications. It begins by defining data mining as the process of extracting useful patterns from large amounts of data. The document then outlines the typical steps in the knowledge discovery process, including data selection, preprocessing, transformation, mining, and evaluation. It classifies data mining techniques into predictive and descriptive methods. Specific techniques discussed include classification, clustering, prediction, and association rule mining. Finally, the document discusses applications of data mining in fields like healthcare, biology, retail, and banking.
The development of data mining is inseparable from the recent developments in information technology that enables the accumulation of large amounts of data. For example, a shopping mall that records every sales transaction of goods using various POS (point of sales). Database data from these sales could reach a large storage capacity, even more being added each day, especially when the shopping center will develop into a nationwide network. The development of the internet at the moment also has a share large enough in the accumulation of data occurs. But the rapid growth of data accumulation it has created conditions that are often referred to as "data rich but information poor" because the data collected can not be used optimally for useful applications. Not infrequently the data set was left just seemed to be a "grave data". There are several techniques used in data mining which includes association, classification, and clustering. In this paper, the author will do a comparison between the performance of the technical classification methods naïve Bayes and C4.5 algorithms.
An Efficient Approach for Asymmetric Data ClassificationAM Publications
In many classification problems, the number of targets (eg. intruders) present is very small compared with
the number of clutter objects. Traditional classification approaches usually ignore this class imbalance, causing
performance to experience low accordingly. In contrary, the algorithm considerably imbalanced logistic regression
(IILR) algorithm explicitly addresses class imbalance in its formulation. I am proposing this algorithm and give the
details necessary to employ it for intrusion detection data sets characterized by class imbalance.
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The document discusses data mining and knowledge discovery in databases. It defines data mining as the nontrivial extraction of implicit and potentially useful information from large amounts of data. With huge increases in data collection and storage, data mining aims to analyze data and discover patterns that can provide insights and knowledge about businesses and the real world. The data mining process involves selecting, preprocessing, transforming, and analyzing data to extract hidden patterns and relationships, which are then interpreted and evaluated.
Data mining techniques are used to analyze large datasets and discover hidden patterns. There are three main types of data mining techniques: supervised, unsupervised, and semi-supervised learning. Supervised learning uses labeled training data to learn relationships between inputs and outputs. Unsupervised learning looks for patterns in unlabeled data. Semi-supervised learning uses some labeled and mostly unlabeled data. The knowledge discovery in databases (KDD) process is a nine step method for applying data mining techniques which includes data selection, preprocessing, transformation, mining, and interpretation.
The past two decades has seen a dramatic increase in the amount of information or data being stored in electronic format. This accumulation of data has taken place at an explosive rate. It has been estimated that the amount of information in the world doubles every 20 months and the size and number of databases are increasing even faster. The increase in use of electronic data gathering devices such as point-of-sale or remote sensing devices has contributed to this explosion of available data. Figure 1 from the Red Brick company illustrates the data explosion.
presentation on recent data mining Techniques ,and future directions of research from the recent research papers made in Pre-master ,in Cairo University under supervision of Dr. Rabie
Data Mining is an important aspect for any business. Most of the management level decisions are based on the process of Data Mining. One of such aspect is the association between different sale products i.e. what is the actual support of a product respected to the other product. This concept is called Association Mining. According to this concept we define the process of estimating the sale of one product respective to the other product. We are proposing an association rule based on the concept of Hardware support. In this concept we first maintain the database and compare it with systolic array after this a pruning process is being performed to filter the database and to remove the rarely used items. Finally the data is indexed according to hashing technique and the decision is performed in terms of support count. Krishan Rohilla | Shabnam Kumari | Reema"Data Mining based on Hashing Technique" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 2017, URL: http://www.ijtsrd.com/papers/ijtsrd82.pdf http://www.ijtsrd.com/computer-science/data-miining/82/data-mining-based-on-hashing-technique/krishan-rohilla
This document discusses various data mining techniques, including artificial neural networks. It provides an overview of the knowledge discovery in databases process and the cross-industry standard process for data mining. It also describes techniques such as classification, clustering, regression, association rules, and neural networks. Specifically, it discusses how neural networks are inspired by biological neural networks and can be used to model complex relationships in data.
This document provides an overview of knowledge discovery and data mining in databases. It discusses how knowledge discovery in databases is the process of finding useful knowledge from large datasets, with data mining being the core step that extracts patterns from data. The document outlines the common steps in the knowledge discovery process, including data preparation, data mining algorithm selection and employment, pattern evaluation, and incorporating discovered knowledge. It also describes different data mining techniques such as prediction, classification, and clustering and their goals of extracting meaningful information from data.
EXPLORING DATA MINING TECHNIQUES AND ITS APPLICATIONSeditorijettcs
Dr.T.Hemalatha#1, Dr.G.Rashita Banu#2, Dr.Murtaza Ali#3
#1.Assisstant.Professor,VelsUniversity,Chennai
#2Assistant Professor,Department of HIM&T,JazanUniversity,Jasan
#3HOD, Department of HIM&T JazanUniversity,Jasan
This slide describe all the necessary topic on Data-Mining. Even this covered all the important Questions on Data Mining in Graduation Level. Basically it covers the actual 2 and 4 marks questions along with the answers that you will need after.
This document discusses using data mining techniques like machine learning to analyze air quality data and generate models for predicting pollution levels. It summarizes applying decision trees and neural networks to data on pollutants and weather factors in Cambridge, UK. The models showed air temperature as the dominant predictor of ozone levels. While data mining provided insights, the author notes it is most useful complementing existing scientific domain knowledge and physical models of air quality.
This document discusses using data mining techniques like machine learning to analyze air quality data and generate models for predicting pollution levels. It summarizes applying decision trees and neural networks to data on pollutants and weather in Cambridge, UK. The models showed air temperature is a dominant predictor of ozone levels. While data mining provides an empirical approach and short-term predictions, physical models are still needed to fully understand urban air quality given small-scale variations.
McKinsey Global Institute Big data The next frontier for innova.docxandreecapon
McKinsey Global Institute
Big data: The next frontier for innovation, competition, and productivity 27
2. Bigdatatechniquesand technologies
A wide variety of techniques and technologies has been developed and adapted to aggregate, manipulate, analyze, and visualize big data. These techniques and technologies draw from several fields including statistics, computer science, applied mathematics, and economics. This means that an organization that intends to derive value from big data has to adopt a flexible, multidisciplinary approach. Some techniques and technologies were developed in a world with access to far smaller volumes and variety in data, but have been successfully adapted so that they are applicable to very large sets of more diverse data. Others have been developed more recently, specifically to capture value from big data. Some were developed by academics and others by companies, especially those with online business models predicated on analyzing big data.
This report concentrates on documenting the potential value that leveraging big data can create. It is not a detailed instruction manual on how to capture value, a task that requires highly specific customization to an organization’s context, strategy, and capabilities. However, we wanted to note some of the main techniques and technologies that can be applied to harness big data to clarify the way some
of the levers for the use of big data that we describe might work. These are not comprehensive lists—the story of big data is still being written; new methods and tools continue to be developed to solve new problems. To help interested readers find a particular technique or technology easily, we have arranged these lists alphabetically. Where we have used bold typefaces, we are illustrating the multiple interconnections between techniques and technologies. We also provide a brief selection of illustrative examples of visualization, a key tool for understanding very large-scale data and complex analyses in order to make better decisions.
TECHNIQUES FOR ANALYZING BIG DATA
There are many techniques that draw on disciplines such as statistics and computer science (particularly machine learning) that can be used to analyze datasets. In this section, we provide a list of some categories of techniques applicable across a range of industries. This list is by no means exhaustive. Indeed, researchers continue to develop new techniques and improve on existing ones, particularly in response to the need
to analyze new combinations of data. We note that not all of these techniques strictly require the use of big data—some of them can be applied effectively to smaller datasets (e.g., A/B testing, regression analysis). However, all of the techniques we list here can be applied to big data and, in general, larger and more diverse datasets can be used to generate more numerous and insightful results than smaller, less diverse ones.
A/B testing. A technique in which a control group is compa ...
McKinsey Global Institute Big data The next frontier for innova.docx
Literature%20 review
1. Rhodes University
COMPUTER SCIENCE HONOURS PROJECT
Literature Review
Data Mining with Oracle 10g using Clustering and
Classification Algorithms
By: Nhamo Mdzingwa
Supervisor: John Ebden
Date: 30 May 2005
Abstract
The field of data mining is concerned with learning from data or rather turning data into
information. It is a creative process requiring a unique combination of tools for each
application. However, the commercial world is fast reacting to the growth and potential
in this area as a wide range of tools are marketed under the label of data mining. This
literature survey will explore some of the ad hoc methodology generally used for data
mining in the commercial world mainly focusing on the data mining process and data
mining algorithms used. It will also include a brief description of the Oracle data mining
tool.
1. Introduction
Data mining has been defined by [Han et al, 2001], [Roiger et al, 2003] and [Mannila et
al, 2001] as a process of extracting or mining knowledge from large amounts of data, or
simply knowledge discovery in databases. It has become useful over the past decade in
business to gain more information, to have a better understanding of running a business,
and to find new ways and ideas to extrapolate a business to other markets [Verhees,
2002]. Below [Palace, 1996] gives a fundamental example were data mining was used:
• One Midwest grocery chain used the data mining capacity of Oracle software to
analyze local buying patterns. They discovered that when men bought diapers on
Thursdays and Saturdays, they also tended to buy beer. Further analysis showed that
these shoppers typically did their weekly grocery shopping on Saturdays. On
Thursdays, however, they only bought a few items. The retailer concluded that they
purchased the beer to have it available for the upcoming weekend. The grocery chain
could use this newly discovered information in various ways to increase revenue. For
example, they could move the beer display closer to the diaper display. And, they
could make sure beer and diapers were sold at full price on Thursdays.
2. It is however necessary to examine the algorithms that are put into practice when
conducting the data mining process with more emphasis on accuracy and efficiency.
1.2 Classification of Data Mining
There is a wide range of sources available on data mining and most of these have various
ways of implementing the data mining process, with most sources classifying data mining
into categories. Authors find it convenient to categorise data mining corresponding to
different objectives for the person analysing the data. According to [Roiger et al, 2003],
data mining is classified into supervised and unsupervised concept learning methods.
Supervised learning builds classification models by forming concept definitions from sets
of data containing predefined classes while unsupervised clustering builds models from
data without the aid of predefined classes were data instances are grouped together based
on a similarity scheme defined by the clustering system. The authors also mention that
supervised learning builds models by using input attributes to predict output attribute
values while in unsupervised learning no target attributes are produced but rather giving a
descriptive relationship by using an objective function to extract clusters in the input data
or particular features which are useful for describing the data.
[Berry et al, 2000] also categorises data mining into directed data mining and undirected
data mining as the two main styles of data mining. According to [Berry et al, 2000] the
goal in directed data mining is to use the available data set to build a model that describes
one particular variable of interest in terms of the rest of the available data. The authors
also point out that directed data mining often takes the form of predictive modelling,
where one knows what he want to predict. Classification, prediction and estimation are
the techniques used in directed data mining. In undirected data mining, no variable is
singled out as the target. The goal is to establish some relationship among all the
variables. Examples of this type include clustering, association rules, description and
visualisation. [Berry et al, 2000]
[Mannila et al, 2001] gives three general categorises of data mining namely; Exploratory
Data Analysis (EDA), Descriptive Modelling and Predictive Modelling. With EDA the
goal is to explore the data without any idea of what one is looking for and typical
techniques are interaction and visualisation. A descriptive model presents the main
features of the data. It is essentially a summary of the data permitting the study of the
important aspects of the data. Clustering techniques are used in this category. In contrast,
a predictive model has the specific objective of allowing one to predict the value of some
target characteristic of an object on the basis of observed values of other characteristics of
the object. This category includes techniques such as Classification and regression.
From the above, it is clear that the classified categories described by the different authors
all involve similar techniques. We can then say that directed data mining, supervised
learning and predictive modelling of data mining describe similar techniques that can be
referred to as supervised learning. Unsupervised learning, undirected data mining and
descriptive modelling are techniques in the same category and will be referred to as
unsupervised learning.
3. 2 Data mining algorithms
Most data mining tools are based on the use of algorithms to implement the categories
described above. Supervised learning covers techniques that include classification,
estimation, prediction, decision trees and association rules. Unsupervised learning covers
techniques that include clustering, association rule induction, neural networks and
association discovery or market basket analysis.
2.1 Supervised learning
As stated in 1.2, supervised learning requires target attributes to be identified. The
supervised learning technique then sifts through the data trying to find patterns between
independent attributes (predictors) and the dependent attribute, then builds a model that
best represent the functional relationships. [Pyle, 2000] Typically, for the data mining
process, the data is separated into two parts; one for training and another for testing. The
initial model is built using the first sample of the data and then the model is applied to the
second sample to evaluate the accuracy of the model’s predictions.
2.1.1 Classification Algorithms
[Han et al, 2001] describes classification as a model built describing a predetermined set
of data classes or concepts. [Roiger et al, 2003] also describes classification as a
technique where the dependent or output variable is categorical. The emphasis is on
building a model able to assign new instances of data to categorical classes. Classification
Algorithms are comprised of the Naive Bayes, Adaptive Bayes Network supporting
decision trees, Model Seeker
2.1.1.1 Naïve Bayes
According to [Berger, 2004], the Naïve Bayes algorithm builds models that predict the
probability of specific outcomes. Naïve Bayes algorithm achieves this by finding patterns
and relationships in the data by counting the number of times various conditions are
observed. It then builds a data mining model to represent those patterns and relationships.
The data mining model represents these relationships and can be applied to new data to
make predictions. Naïve Bayes algorithm makes predictions using Bayes’ Theorem, a
statistical theorem in nature. It assumes that the effect of an attribute value on a given
class is independent of the values of other attributes (class conditionally independence)
[Han et al, 2001]
[Berger, 2004] also emphasises that the algorithm provides quicker model building and
faster application to new data than the Adaptive Bayes Network algorithm. [Han et al,
2001] points out that Basian classifiers also are known as the naïve Basian classifier have
exhibited high accuracy and speed when applied to large databases. Naïve Bayes can also
be used to make predictions of categorical classes that consist of binary-type outcomes or
multiple categories of outcomes [Berger, 2004]. In attempting to answer the question:
“how effective are Bayesian classifies” [Han et al, 2001] indicates that in theory they
have minimum error in comparison to other techniques. The authors further indicate that
4. in practice this is not always the case owing to inaccuracies in the assumptions made for
its use, such as conditional independence and the lack of availability of probability data.
2.1.1.2 Adaptive Bayes Network
According to [Berger, 2004] Adaptive Bayes Network (ABN) algorithm is similar to
Naïve Bayes and, depending on the data being analyzed, can possibly produce better
models. They can also be used to generate rules or decision tree-like outcomes when built
and again to make predictions when applied to new data. The rules that are generated are
easy to interpret in the form of “if…..then” statements but [Berger 2004] states that it
does involve a larger number of parameters to be set and it tends to take a longer time to
build such a model. An additional benefit of ABN models is that they are able to produce
simple “rules” that may provide insight as to why the prediction was made. A typical
“prediction” and “rule” might be:
[Berger, 2004]
Prediction: BMW = “YES”
ABN Rule: >30 AGE >40 and INCOME = High
Confidence: = 85% (634 cases fit this profile, 539 purchased BMW autos)
Support = .00543 (539 cases out of 99,263 records)
2.1.1.3 Tree algorithms
A decision tree is a flow-chart-like tree structure, with internal nodes representing an
attribute. In decision tree data mining, a record flows through the tree along a path
determined by a series of tests until a terminal node is reached and it is then given a class
label. Decision trees are useful for classification and predictions as they assign records to
broad categories and output rules that can be easily translated. Different criteria are used
to determine when splits in the tree occur [Han et al, 2001]. The CART (classification
and regression trees) incorporates the machine learning algorithms which generates
binary trees as it rates high on statistical prediction. CART divides a data set on the basis
of variety to determine the best separators [Mannila et al, 2001]. The efficiency of exiting
decision tree algorithms has been established for relatively small data sets. [Han et al,
2001] points out that efficiency and scalability become issues of concern when these
algorithms are applied to the mining of large databases.
2.1.1.4 Association rules
Association rule mining searches for interesting relationships among items in a given data
set. Market basket analysis is just one form of association rule mining [Han et al, 2001].
According to [Al-Attar, 2004], association rules are similar to decision trees and
association rule induction is the most established and effective of the current data mining
technologies. This technique involves the definition of a business goal and the use of rule
induction to generate patterns relating this goal to other data fields. The patterns are
generated as trees with splits on data fields. This technique allows the user to add their
domain knowledge to the process and decide on attributes for generating splits [Han et al,
2001].
5. 2.2 Unsupervised learning
With unsupervised learning, the user does not specify a target attribute for the data
mining algorithm. Unsupervised learning techniques such as associations and clustering
algorithms make no assumptions about a target field. Instead, they allow the data mining
algorithm to find associations and clusters in the data independent of any a priori defined
business objective. [Berger, 2004]
2.2.1 Clustering
[Berry et al, 2000] defines clustering as the task of segmenting a diverse group of
attributes into a number of more similar subgroups or clusters. What distinguishes
clustering from classification is that clustering does not rely on predefined classes.
[Roiger et al, 2003] say clustering is useful for determining if meaningful relationships
exist in the data, evaluating the performance of supervised learning models, detecting
outliers in the data and even determining input attributes for supervised learning.
2.2.1.1 Clustering algorithms
Enhanced k-Means and Orthogonal Partitioning Clustering (O-Cluster)
Enhanced k-Means (EKM) and O-Cluster algorithms support identifying naturally
occurring groupings within the data population EKM algorithm supports hierarchical
clusters, handles numeric attributes and will cut the population into the user specified
number of clusters. The algorithm divides the data set into k number of clusters according
to the location of all members of a particular cluster in the data. Clustering makes use of
the Euclidean distance formula to determine the location of data instances and their
position in clusters and so requires numerical values that have been properly scaled.
When choosing the number of clusters to create it is possible to choose a number that
doesn’t match the natural structure of the data which leads to poor results. For this reason
[Berry et al, 2000] say it is often necessary to experiment with the number of clusters to
be used. O-cluster algorithm handles both numeric and categorical attributes and will
automatically select the best cluster definitions. [Berger, 2004]
2.2.2 Neural network
Neural network algorithm is also enveloped as unsupervised learning technique.
According to [Berry et al, 2000] neural networks are the most widely used known and the
least understood of the major data mining techniques. [Pyle, 2000] describes neural
networks as network construction network system of interconnected interacting weights
at each node acting as input and output stations. Each input to the network gets its own
node which consists of a transformation of input variables fed in. The input unit is
connected to the output unit with a weighting and the input is combined in the output unit
with a combination function. The activation function is the passed transfer function.
[Berry et al, 2000] says training a neural network is a process that involves setting
weights on inputs to best approximate a target variable. This is important for optimizing
6. the neural network. Three steps are involved in training. Training instance variables,
calculating outputs using existing weights and calculating errors and accordingly
adjusting weights.
[Berry et al, 2000] further elaborates that neural networks, neural networks are not easy
to use and understand but they produce very good results. The authors continue to say
that neural networks require extensive data preparation as inputs must be scaled,
categorical data must be converted to numerical data without introducing any ordering
and missing values must be dealt with. The authors suggest that a problem with neural
networks is that the results cannot be explained and so they should be used when results
are more useful than understanding and not when there are a high number of inputs.
The figure below by [Elder, 1998] shows some useful properties employed by some of
the algorithms described:
3 Data Mining Process
There is increased interest in a process or methodology for data mining. This process is
another important aspect that needs to be examined as it layouts clear steps that can be
followed in the data mining process. It is argued that such a formalised process will
widen the exploitation of data mining as an enabling technology for solving business
7. problems. It will allow people with varying expertise in data mining and from different
business sectors to carry out successful data mining projects with a high degree of
consistency. [Al-Attar, 2004]
[Berger, 2004] believes that to be effective in data mining, successful data analysts
generally following a four step process:
1) Problem definition -This is the most important step and is where the domain
expert decides the specifics of translating an abstract business objective e.g. “How
can I sell more of my product to customers?” into a more tangible and useful data
mining problem statement e.g. “Which customers are most likely to purchase
product A?” To build a predictive model that predicts who is most likely to buy
product A, we first must have data that describes the customers who have
purchased product A in the past. Then we can begin to prepare the data for
mining.
2) Data gathering and preparation In this step, we take a closer look at our
available data and determine what additional data we will need to address our
business problem. We often begin by working with a reasonable sample of the
data, e.g., hundred of records (rare, except in some life sciences cases) to many
thousands or millions of cases (more typical for business-to-consumer cases).
Some processing of the data to transform for example a “Date_of_Birth” field into
“AGE” and to derive fields such as “Number_of_times_Amount_Exceeds_100” is
performed to attempt the “tease the hidden information closer to the surface of the
data” for easier mining.
3) Model building and evaluation Once steps 1 and 2 have been properly
completed, this step is where the data mining algorithms sift through the data to
find patterns and to build predictive models. Generally, a data analyst will build
several models and change mining parameters in an attempt to build the best or
most useful models.
4) Knowledge deployment Once a useful model that adequately models the data has
been found, you want to distribute the new insights and predictions to others—
managers, call center representatives, and executives.
[Roiger et al, 2003] also elaborate a data mining process where emphasis is placed on
data preparation for model building.
1) Goal identification. Properly identifying goals to be accomplished by the data
mining project help with domain understanding and determining what is to be
accomplished.
2) Creating the target data. It is emphasized that at this stage a human expert is
required to choose the initial data for the project.
8. 3) Data preprocessing in order to deal with noisy data. This stage involves
locating duplicate records in the data set, locating incorrect attributes, smoothing
the data and dealing with outliers in the data set. It includes data transformation
which involves the addition or removal of attributes and instances, normalizing of
data and type conversions.
4) The actual data mining -At this stage the model is built from training and test
data sets. The resulting model is then interpreted to determine if the results it
presents are useful or interesting. The model or acquired knowledge is then
applied to the problem.
[Verhees, 2002] gives a brief methodology for conducting data mining also identifying
problems normally associated with the process. The problems include the nature of data
in the database as it is often incomplete, noisy or very large, inadequate or irrelevant.
Also included are the errors in the stored data. The steps should include:
1) Problem analysis- here that is when it will be determined whether the problem is
suitable for data mining and what data and technologies are available. Also at this
stage it will be important to determine what will be done with the results of the
data mining to put the problem in context.
2) Data preparation- should be part of the methodology and data processing.
Processing involves pre-processing or cleansing of the data, data integration,
variable transformation and splitting or sampling from the database.
3) Data exploration-. This allows the analyst or data miner to discover the
unexpected in the data as well as to confirm the expected.
4) Pattern generation-should follow which involves applying the algorithms and
validating and interpreting the patterns that result.
5) Model validation-is required in order to confirm the usability of the developed
model. Validation can be conducted using a validation data set and assesses the
quality of the model fit to the data as well as protecting the model from over- or
under-fitting.
6) Interpretation and decision making-conclude the methodology and attempt to
transform the patterns discovered during data mining into knowledge.
There are a number of initiatives for the development of a formal/documented data
mining process in the world. It is reassuring to the data mining community that the
processes emerging from all of these initiatives reveal a large degree of similarity. There
is widespread agreement on the main steps or stages involved in such a process and any
differences relate only to the detailed tasks within each stage. [Al-Attar, 2004] gives a
summary of the major stages of a data mining process is:
9. 1) Goal definition-This involves defining the goal or objective for the data mining
project. This should be a business goal or objective which normally relates to a
business event such as arrears in mortgage repayment, customer attrition (churn),
energy consumption in a process, etc. This stage also involves the design of how
the discovered patterns would be utilised as part of the overall business solution.
2) Data selection- This is the process of identifying the data needed for the data
mining project and the sources of this data.
3) Data preparation- This involves cleansing the data, joining/merging data sources
and the derivation of new columns (fields) in the data through aggregation,
calculations or text manipulation of existing data fields. The end result is
normally a flat table ready for the application of the data mining itself (i.e. the
discovery algorithms to generate patterns). Such a table is normally split into two
data sets; one set for pattern discovery and one set for pattern verification.
4) Data exploration- This involves the exploration of the prepared data to get a
better feel prior to pattern discovery and also to validate the results of the data
preparation. Typically, this involves examining the statistics (minimum,
maximum, average, etc.) and the frequency distribution of individual data fields.
It also involves field versus field graphs to understand the dependency between
fields.
5) Pattern Discovery- This is the stage of applying the pattern discovery algorithm
to generate patterns. The process of pattern discovery is most effective when
applied as an exploration process assisted by the discovery algorithm. This allows
business users to interact with and to impart their business knowledge to the
discovery process. In the case of inducing a tree, users can at any point in the tree
construction examine / explore the data filtering to that path, examine the
recommendation of the algorithm regarding the next data field to use for the next
branch then use their business judgement to decide on the data field for branching.
The pattern discovery stage also involves analysing the ability of the discovered
patterns to predict the propensity of the business event, and for verification
against an independent data set.
6) Pattern deployment- This stage involves the application of the discovered
patterns to solve the business goal of the data mining project. This can take many
forms:
o Patterns presentation: The description of the patterns (or the graphical
tree display) and their associated data statistics are included in a document
or presentation.
o Business intelligence: The discovered patterns are used as queries against
a data base to derive business intelligence reports. This requires the data
mining tool to generate SQL representations of the decision tree.
o Data Scoring & Labelling: The discovered patterns are used to score
and/or label each data record in the database with the propensity and the
label of the pattern it belongs to. This can be done directly by the data
mining tool or through generation of SQL or C representation of the
decision tree
10. o Alarm monitoring: The discovered patterns are used as 'norms' for a
business process. Monitoring these patterns will enable deviations from
normal conditions to be detected at the earliest possible time. This can be
achieved by embedding the data mining tool as a monitoring component,
or through using SQL generated by the data mining tool.
7) Pattern Validity monitoring- As a business process changes over time, the
validity of patterns discovered from historic data will deteriorate. It is therefore
important to detect these changes at the earliest possible time by monitoring
patterns with new data. Significant changes to the patterns will point to the need
to discover new patterns from more recent data.
4 Oracle Data Mining Tool
Oracle Data Mining is a powerful data mining software embedded in the 10g Database
Enterprise Edition (EE) that enables you to discover new insights hidden in your data
[Berger, 2004]. The Oracle Data Mining suite is made up of two components, the data
mining Java API and the Data Mining Server (DMS). The DMS is a server-side, in-
database component that performs data mining that is easily available and scalable. The
DMS also provides a repository of metadata of the input and result objects of data
mining.
As stated by [Berger, 2004] Oracle Data Mining supports supervised learning techniques
(classification, regression, and prediction problems), unsupervised learning techniques
(clustering, associations, and feature selection problems), attribute importance techniques
(find the key variables), text mining, and has a special algorithm for life sciences
sequence searching and alignment problems.
Oracle Data Mining can scale to the size of the problem by adding hardware or switching
to more powerful platforms. Oracle Data Mining takes advantage of Oracle's parallelism
for faster computing by leveraging Oracle's Real Application Clusters (RAC) technology.
[Oracle, 2005]
Application developers access Oracle Data Mining's functionality through a Java-based
or PL/SQL interface. Programmatic control of all data mining functions enables
automation of data preparation, model-building, and model-scoring operations in
production applications. [Oracle, 2005]
Choice of algorithm for Oracle Data Mining depends on the data available for mining as
well as the types of results and conclusions required. [Berger, 2004] continues to give a
discussion on when it is applicable to use the various algorithms; the states that if there is
a set of input data and output data, then it is more likely that supervised learning will be
used since input and output attributes exist. [Hand, Mannila and Smyth, 2001] also states
that if the input and output data is numerical or categorical and have interesting
11. interactions association rules are recommended. On the other hand, [Trueblood and
Lovett, Jnr] say that if the data sets have missing values neural networks may be a good
choice. [Al-Attar, 2004] further states that when maximum accuracy is required of a
model, it is helpful to create multiple models using the same data mining technique until
the best model is created.
5 Conclusion
Data mining is becoming a strategically important area for many business organisations,
and due to its applied importance, however, the field emerges as a rapidly growing and
major area [Verhees, 2002]. It can thus be concluded that data mining is a step wise
process that requires the insight and experience of the data miner. The process is also
supported by the use of various software tools available for data mining
12. References:
[Al-Attar, 2004] White Paper: Data Mining - Beyond Algorithms, Dr
Akeel Al- Attar, 2004,
<http://www.attar.com/tutor/mining.htm>
Accessed: 10 April 2005
[Berger, 2004] Berger, C., 09/2004, Oracle Data Mining, Know
More, Do More, Spend Less - An Oracle White
Paper, URL:
<http://www.oracle.com/technology/products/bi/od
m/pdf/bwp_db_odm_10gr1_0904.pdf>, Accessed:
14 April 2005
[Berry et al, 2000] Mastering Data Mining: The Art and Science of
Customer Relationship Management, Michael J.A.
Berry and Gordon S. Linoff, USA, Wiley Computer
Publishing, 2000
[Han et al, 2001] Data mining: concepts and techniques by Jiawei
Han and Micheline Kamber, San Francisco,
California, Morgan Kauffmann, 2001.
[Mannila et al, 2001] David Hand, Heikki Mannila and Padhraic Smyth,
Principles of data mining, Cambridge
Massachusetts, MIT Press, 2001.
[Oracle, 2005] The Oracle Home Page. Revised February 2005.
Oracle 10g Data Mining FAQ
http://www.oracle.com/technology/products/bi/odm
/odm_10g_faq.html#api
Accessed: 17 May 2005
[Palace, 1996] Bill Palace , Spring 1996. “Data Mining: What is
Data Mining?” Anderson Graduate School of
Management at UCLA:
http://www.anderson.ucla.edu/faculty/jason.
frand/teacher/technologies/palace/datamining.ht
m Accessed: 15 April 2005
13. [Paul et al, 2002] Preparing and Mining Data in Microsoft SQL
Server 2000 and Analysis Services, Seth Paul, Nitin
Gautam, Raymond Ballint, Published: September
2002, Updated: January 2003
[Pyle, 2000] Data Preparation for Data Mining: Dorian Pyle,
San Francisco, California, Morgan Kauffman, 2000.
[Roiger et al, 2003] Data mining: a tutorial- based primer by Richard J.
Roiger and Michael W. Geatz, Boston,
Massachusetts, Addison Wesley, 2003..
[Trueblood et al, Jnr] Robert P. Trueblood and John N. Lovett, Jnr. Data
Mining and Statistical Analysis Using SQL, USA,
Apress, 2001
[Verhees, 2002] Enhance your Application – Simple Integration of
Advanced Data Mining Functions, Corinne
Baragoin, Ronnie Chan, Helena Gottschalk,
Gregor Meyer, Paulo Pereira, Jaap Verhees, 2002,
<http://www.redbooks.ibm.com/redbooks/SG24687
9.html> Accessed: 12 April 2005
[Elder, 1998] A Comparison of Leading Data Mining Tools-Elder
Research. John F. Elder IV & Dean W. Abbott, last
updated August 28, 1998
http://www.datamininglab.com/pubs/kdd98_elder_a
bbott_nopics_bw.pdf Accessed: 15 May 2005