Existing parallel digging calculations for visit itemsets do not have a component that empowers programmed parallelization, stack adjusting, information conveyance, and adaptation to non-critical failure on substantial bunches. As an answer for this issue, we outline a parallel incessant itemsets mining calculation called FiDoop utilizing the MapReduce programming model. To accomplish compacted capacity and abstain from building contingent example bases, FiDoop joins the incessant things Ultrametric tree, as opposed to ordinary FP trees. In FiDoop, three MapReduce occupations are actualized to finish the mining undertaking. In the essential third MapReduce work, the mappers autonomously disintegrate itemsets, the reducers perform mix activities by building little Ultrametric trees, and the genuine mining of these trees independently. We actualize FiDoop on our in-house Hadoop group. We demonstrate that FiDoop on the group is touchy to information dissemination and measurements, in light of the fact that itemsets with various lengths have diverse decay and development costs. To enhance FiDoop's execution, we build up a workload adjust metric to quantify stack adjust over the group's registering hubs. We create FiDoop-HD, an augmentation of FiDoop, to accelerate the digging execution for high-dimensional information investigation. Broad tests utilizing genuine heavenly phantom information exhibit that our proposed arrangement is productive and versatile.
Parallel Key Value Pattern Matching Modelijsrd.com
Mining frequent itemsets from the huge transactional database is an important task in data mining. To find frequent itemsets in databases involves big decision in data mining for the purpose of extracting association rules. Association rule mining is used to find relationships among large datasets. Many algorithms were developed to find those frequent itemsets. This work presents a summarization and new model of parallel key value pattern matching model which shards a large-scale mining task into independent, parallel tasks. It produces a frequent pattern showing their capabilities and efficiency in terms of time consumption. It also avoids the high computational cost. It discovers the frequent item set from the database.
Construction of a compact FP-tree ensures that subsequent mining can be performed with a rather compact data structure. This does not automatically guarantee that it will be highly efficient since one may still encounter the combinatorial problem of candidate generation if one simply uses this FP-tree to generate and check all the candidate patterns. we study how to explore the compact information stored in an FP-tree, develop the principles of frequent-pattern growth by examination of our running example, explore how to perform further optimization when there exit a single prefix path in an FP-tree, and propose a frequent- pattern growth algorithm, FP-growth, for mining the complete set of frequent patterns using FP-tree.
Graph based Approach and Clustering of Patterns (GACP) for Sequential Pattern...AshishDPatel1
The sequential pattern mining generates the sequential patterns. It can be used as the input of another program for retrieving the information from the large collection of data. It requires a large amount of memory as well as numerous I/O operations. Multistage operations reduce the efficiency of the
algorithm. The given GACP is based on graph representation and avoids recursively reconstructing intermediate trees during the mining process. The algorithm also eliminates the need of repeatedly scanning the database. A graph used in GACP is a data structure accessed starting at its first node called root and each node of a graph is either a leaf or an interior node. An interior node has one or more child nodes, thus from the root to any node in the graph defines a sequence. After construction of the graph the pruning technique called clustering is used to retrieve the records from the graph. The algorithm can be used to mine the database using compact memory based data structures and cleaver pruning methods.
A Hybrid Algorithm Using Apriori Growth and Fp-Split Tree For Web Usage Mining iosrjce
Internet is the most active and happening part of everyone’s life today. Almost every business or
service or organization has its website and performance of the site is an important issue. Web usage mining
based on web logs is an important methodology for optimizing website’s performance over the internet.
Different mining techniques like Apriori method, FP Tree methodology, K-Means method etc. have been
proposed by different researchers in order to make the data mining more effective and efficient. Many people
have modeled Apriori or FP Tree in their own way to increase data mining productiveness. Wu proposed
Apriori Growth as a hybrid of Apriori and FP Tree algorithm and improved FP Tree by mining using Apriori
and removed the complexity involved in FP Growth mining. Lee proposed FP Split Tree as a variant of FP Tree
and reduced the complexity by scanning the database only once against twice in FP Tree method. This research
proposes a new hybrid algorithm of FP Split and Apriori growth which combines the positives of both the
algorithms to create a new technique which provides with a better performance over the traditional methods.
The new proposed algorithm was implemented in java language on web logs obtained from IIS server and the
computational results of the proposed method performs better than traditional FP Tree method, Apriori
Method.
Weighted frequent pattern mining is suggested to find out more important frequent pattern by considering different weights of each item. Weighted Frequent Patterns are generated in weight ascending and frequency descending order by using prefix tree structure. These generated weighted frequent patterns are applied to maximal frequent item set mining algorithm. Maximal frequent pattern mining can reduces the number of frequent patterns and keep sufficient result information. In this paper, we proposed an efficient algorithm to mine maximal weighted frequent pattern mining over data streams. A new efficient data structure i.e. prefix tree and conditional tree structure is used to dynamically maintain the information of transactions. Here, three information mining strategies (i.e. Incremental, Interactive and Maximal) are presented. The detail of the algorithms is also discussed. Our study has submitted an application to the Electronic shop Market Basket Analysis. Experimental studies are performed to evaluate the good effectiveness of our algorithm..
Parallel Key Value Pattern Matching Modelijsrd.com
Mining frequent itemsets from the huge transactional database is an important task in data mining. To find frequent itemsets in databases involves big decision in data mining for the purpose of extracting association rules. Association rule mining is used to find relationships among large datasets. Many algorithms were developed to find those frequent itemsets. This work presents a summarization and new model of parallel key value pattern matching model which shards a large-scale mining task into independent, parallel tasks. It produces a frequent pattern showing their capabilities and efficiency in terms of time consumption. It also avoids the high computational cost. It discovers the frequent item set from the database.
Construction of a compact FP-tree ensures that subsequent mining can be performed with a rather compact data structure. This does not automatically guarantee that it will be highly efficient since one may still encounter the combinatorial problem of candidate generation if one simply uses this FP-tree to generate and check all the candidate patterns. we study how to explore the compact information stored in an FP-tree, develop the principles of frequent-pattern growth by examination of our running example, explore how to perform further optimization when there exit a single prefix path in an FP-tree, and propose a frequent- pattern growth algorithm, FP-growth, for mining the complete set of frequent patterns using FP-tree.
Graph based Approach and Clustering of Patterns (GACP) for Sequential Pattern...AshishDPatel1
The sequential pattern mining generates the sequential patterns. It can be used as the input of another program for retrieving the information from the large collection of data. It requires a large amount of memory as well as numerous I/O operations. Multistage operations reduce the efficiency of the
algorithm. The given GACP is based on graph representation and avoids recursively reconstructing intermediate trees during the mining process. The algorithm also eliminates the need of repeatedly scanning the database. A graph used in GACP is a data structure accessed starting at its first node called root and each node of a graph is either a leaf or an interior node. An interior node has one or more child nodes, thus from the root to any node in the graph defines a sequence. After construction of the graph the pruning technique called clustering is used to retrieve the records from the graph. The algorithm can be used to mine the database using compact memory based data structures and cleaver pruning methods.
A Hybrid Algorithm Using Apriori Growth and Fp-Split Tree For Web Usage Mining iosrjce
Internet is the most active and happening part of everyone’s life today. Almost every business or
service or organization has its website and performance of the site is an important issue. Web usage mining
based on web logs is an important methodology for optimizing website’s performance over the internet.
Different mining techniques like Apriori method, FP Tree methodology, K-Means method etc. have been
proposed by different researchers in order to make the data mining more effective and efficient. Many people
have modeled Apriori or FP Tree in their own way to increase data mining productiveness. Wu proposed
Apriori Growth as a hybrid of Apriori and FP Tree algorithm and improved FP Tree by mining using Apriori
and removed the complexity involved in FP Growth mining. Lee proposed FP Split Tree as a variant of FP Tree
and reduced the complexity by scanning the database only once against twice in FP Tree method. This research
proposes a new hybrid algorithm of FP Split and Apriori growth which combines the positives of both the
algorithms to create a new technique which provides with a better performance over the traditional methods.
The new proposed algorithm was implemented in java language on web logs obtained from IIS server and the
computational results of the proposed method performs better than traditional FP Tree method, Apriori
Method.
Weighted frequent pattern mining is suggested to find out more important frequent pattern by considering different weights of each item. Weighted Frequent Patterns are generated in weight ascending and frequency descending order by using prefix tree structure. These generated weighted frequent patterns are applied to maximal frequent item set mining algorithm. Maximal frequent pattern mining can reduces the number of frequent patterns and keep sufficient result information. In this paper, we proposed an efficient algorithm to mine maximal weighted frequent pattern mining over data streams. A new efficient data structure i.e. prefix tree and conditional tree structure is used to dynamically maintain the information of transactions. Here, three information mining strategies (i.e. Incremental, Interactive and Maximal) are presented. The detail of the algorithms is also discussed. Our study has submitted an application to the Electronic shop Market Basket Analysis. Experimental studies are performed to evaluate the good effectiveness of our algorithm..
Exploratory data analysis of 2017 US Employment data using RChetan Khanzode
Data Science- Exploratory data analysis of year 2017 US Employment data using R – Use Case.Use of R library's for visualization of Employment data by state, county and industry sector - simple Geo spatial data visualization of employment data
Data mining is a very popular research topic over the years. Sequential pattern mining or sequential rule mining is very useful application of data mining for the prediction purpose. In this paper, we have presented a review over sequential rule cum sequential pattern mining. The advantages & drawbacks of each popular sequential mining method is discussed in brief.
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.
In recent years the data mining applications become musty and outmoded over time. Energy wastage is the major
problem more in big data analytics and applications. More workload and more computational time will increase high energy
cost and decrease efficiency. The Incremental computational time processing is a promising approach to refreshing mining
results. It utilizes previously saved states to avoid the expense of re-computation from scratch. In this paper, we propose
Energy efficiency Map Reduce Scheduling Algorithm, a novel incremental processing extension to reduce the Map, the most
widely used framework for mining big data. Map reduce is a programming model for processing and generating large amount of data in parallel processing time. In this paper, Energy Efficiency reduce Map (EEMP) is algorithm provide more energy
and less maps in big data. Priority based scheduling is a task will allocate the schedules based on necessary and utilization of
the Jobs. For reducing the maps, it will reduce the system computational time so easily energy has improved in terms of big data applications.. Final results show the experimental comparison of the different algorithms involved in the paper.
Analytical Study and Newer Approach towards Frequent Pattern Mining using Boo...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
An Improved Frequent Itemset Generation Algorithm Based On Correspondence cscpconf
Association rules play a very vital role in the present day market that especially involves generation of maximal frequent itemsets in an efficient way. The efficiency of association rule is determined by the number of database scans required to generate the frequent itemsets. This in turn is proportional to the time, which will lead to the faster computation of the frequent itemsets. In this paper, a single scan algorithm which makes use of the mapping of the item numbers and array indexing to achieve the generation of the frequent item sets dynamically and faster. The proposed algorithm is an incremental algorithm in that it generates frequent itemsets as and when the data is entered into the database
Distributed Algorithm for Frequent Pattern Mining using HadoopMap Reduce Fram...idescitation
With the rapid growth of information technology and in many business
applications, mining frequent patterns and finding associations among them requires
handling large and distributed databases. As FP-tree considered being the best compact data
structure to hold the data patterns in memory there has been efforts to make it parallel and
distributed to handle large databases. However, it incurs lot of communication over head
during the mining. In this paper parallel and distributed frequent pattern mining algorithm
using Hadoop Map Reduce framework is proposed, which shows best performance results
for large databases. Proposed algorithm partitions the database in such a way that, it works
independently at each local node and locally generates the frequent patterns by sharing the
global frequent pattern header table. These local frequent patterns are merged at final stage.
This reduces the complete communication overhead during structure construction as well as
during pattern mining. The item set count is also taken into consideration reducing
processor idle time. Hadoop Map Reduce framework is used effectively in all the steps of the
algorithm. Experiments are carried out on a PC cluster with 5 computing nodes which
shows execution time efficiency as compared to other algorithms. The experimental result
shows that proposed algorithm efficiently handles the scalability for very large datab ases.
Index Terms—
AN ENHANCED FREQUENT PATTERN GROWTH BASED ON MAPREDUCE FOR MINING ASSOCIATION...IJDKP
In mining frequent itemsets, one of most important algorithm is FP-growth. FP-growth proposes an
algorithm to compress information needed for mining frequent itemsets in FP-tree and recursively
constructs FP-trees to find all frequent itemsets. In this paper, we propose the EFP-growth (enhanced FPgrowth)
algorithm to achieve the quality of FP-growth. Our proposed method implemented the EFPGrowth
based on MapReduce framework using Hadoop approach. New method has high achieving
performance compared with the basic FP-Growth. The EFP-growth it can work with the large datasets to
discovery frequent patterns in a transaction database. Based on our method, the execution time under
different minimum supports is decreased..
Exploratory data analysis of 2017 US Employment data using RChetan Khanzode
Data Science- Exploratory data analysis of year 2017 US Employment data using R – Use Case.Use of R library's for visualization of Employment data by state, county and industry sector - simple Geo spatial data visualization of employment data
Data mining is a very popular research topic over the years. Sequential pattern mining or sequential rule mining is very useful application of data mining for the prediction purpose. In this paper, we have presented a review over sequential rule cum sequential pattern mining. The advantages & drawbacks of each popular sequential mining method is discussed in brief.
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.
In recent years the data mining applications become musty and outmoded over time. Energy wastage is the major
problem more in big data analytics and applications. More workload and more computational time will increase high energy
cost and decrease efficiency. The Incremental computational time processing is a promising approach to refreshing mining
results. It utilizes previously saved states to avoid the expense of re-computation from scratch. In this paper, we propose
Energy efficiency Map Reduce Scheduling Algorithm, a novel incremental processing extension to reduce the Map, the most
widely used framework for mining big data. Map reduce is a programming model for processing and generating large amount of data in parallel processing time. In this paper, Energy Efficiency reduce Map (EEMP) is algorithm provide more energy
and less maps in big data. Priority based scheduling is a task will allocate the schedules based on necessary and utilization of
the Jobs. For reducing the maps, it will reduce the system computational time so easily energy has improved in terms of big data applications.. Final results show the experimental comparison of the different algorithms involved in the paper.
Analytical Study and Newer Approach towards Frequent Pattern Mining using Boo...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
An Improved Frequent Itemset Generation Algorithm Based On Correspondence cscpconf
Association rules play a very vital role in the present day market that especially involves generation of maximal frequent itemsets in an efficient way. The efficiency of association rule is determined by the number of database scans required to generate the frequent itemsets. This in turn is proportional to the time, which will lead to the faster computation of the frequent itemsets. In this paper, a single scan algorithm which makes use of the mapping of the item numbers and array indexing to achieve the generation of the frequent item sets dynamically and faster. The proposed algorithm is an incremental algorithm in that it generates frequent itemsets as and when the data is entered into the database
Distributed Algorithm for Frequent Pattern Mining using HadoopMap Reduce Fram...idescitation
With the rapid growth of information technology and in many business
applications, mining frequent patterns and finding associations among them requires
handling large and distributed databases. As FP-tree considered being the best compact data
structure to hold the data patterns in memory there has been efforts to make it parallel and
distributed to handle large databases. However, it incurs lot of communication over head
during the mining. In this paper parallel and distributed frequent pattern mining algorithm
using Hadoop Map Reduce framework is proposed, which shows best performance results
for large databases. Proposed algorithm partitions the database in such a way that, it works
independently at each local node and locally generates the frequent patterns by sharing the
global frequent pattern header table. These local frequent patterns are merged at final stage.
This reduces the complete communication overhead during structure construction as well as
during pattern mining. The item set count is also taken into consideration reducing
processor idle time. Hadoop Map Reduce framework is used effectively in all the steps of the
algorithm. Experiments are carried out on a PC cluster with 5 computing nodes which
shows execution time efficiency as compared to other algorithms. The experimental result
shows that proposed algorithm efficiently handles the scalability for very large datab ases.
Index Terms—
AN ENHANCED FREQUENT PATTERN GROWTH BASED ON MAPREDUCE FOR MINING ASSOCIATION...IJDKP
In mining frequent itemsets, one of most important algorithm is FP-growth. FP-growth proposes an
algorithm to compress information needed for mining frequent itemsets in FP-tree and recursively
constructs FP-trees to find all frequent itemsets. In this paper, we propose the EFP-growth (enhanced FPgrowth)
algorithm to achieve the quality of FP-growth. Our proposed method implemented the EFPGrowth
based on MapReduce framework using Hadoop approach. New method has high achieving
performance compared with the basic FP-Growth. The EFP-growth it can work with the large datasets to
discovery frequent patterns in a transaction database. Based on our method, the execution time under
different minimum supports is decreased..
In the recent years the scope of data mining has evolved into an active area of research because of the previously unknown and interesting knowledge from very large database collection. The data mining is applied on a variety of applications in multiple domains like in business, IT and many more sectors. In Data Mining the major problem which receives great attention by the community is the classification of the data. The classification of data should be such that it could be they can be easily verified and should be easily interpreted by the humans. In this paper we would be studying various data mining techniques so that we can find few combinations for enhancing the hybrid technique which would be having multiple techniques involved so enhance the usability of the application. We would be studying CHARM Algorithm, CM-SPAM Algorithm, Apriori Algorithm, MOPNAR Algorithm and the Top K Rules.
Web Oriented FIM for large scale dataset using Hadoopdbpublications
In large scale datasets, mining frequent itemsets using existing parallel mining algorithm is to balance the load by distributing such enormous data between collections of computers. But we identify high performance issue in existing mining algorithms [1]. To handle this problem, we introduce a new approach called data partitioning using Map Reduce programming model.In our proposed system, we have introduced new technique called frequent itemset ultrametric tree rather than conservative FP-trees. An investigational outcome tells us that, eradicating redundant transaction results in improving the performance by reducing computing loads.
CLUSTBIGFIM-FREQUENT ITEMSET MINING OF BIG DATA USING PRE-PROCESSING BASED ON...ijfcstjournal
Now a day enormous amount of data is getting explored through Internet of Things (IoT) as technologies
are advancing and people uses these technologies in day to day activities, this data is termed as Big Data
having its characteristics and challenges. Frequent Itemset Mining algorithms are aimed to disclose
frequent itemsets from transactional database but as the dataset size increases, it cannot be handled by
traditional frequent itemset mining. MapReduce programming model solves the problem of large datasets
but it has large communication cost which reduces execution efficiency. This proposed new pre-processed
k-means technique applied on BigFIM algorithm. ClustBigFIM uses hybrid approach, clustering using kmeans algorithm to generate Clusters from huge datasets and Apriori and Eclat to mine frequent itemsets
from generated clusters using MapReduce programming model. Results shown that execution efficiency of
ClustBigFIM algorithm is increased by applying k-means clustering algorithm before BigFIM algorithm as
one of the pre-processing technique.
Now a day enormous amount of data is getting explored through Internet of Things (IoT) as technologies
are advancing and people uses these technologies in day to day activities, this data is termed as Big Data
having its characteristics and challenges. Frequent Itemset Mining algorithms are aimed to disclose
frequent itemsets from transactional database but as the dataset size increases, it cannot be handled by
traditional frequent itemset mining. MapReduce programming model solves the problem of large datasets
but it has large communication cost which reduces execution efficiency. This proposed new pre-processed
k-means technique applied on BigFIM algorithm. ClustBigFIM uses hybrid approach, clustering using kmeans
algorithm to generate Clusters from huge datasets and Apriori and Eclat to mine frequent itemsets
from generated clusters using MapReduce programming model. Results shown that execution efficiency of
ClustBigFIM algorithm is increased by applying k-means clustering algorithm before BigFIM algorithm as
one of the pre-processing technique.
COMPARATIVE STUDY OF DISTRIBUTED FREQUENT PATTERN MINING ALGORITHMS FOR BIG S...IAEME Publication
Association rule mining plays an important role in decision support system. Nowadays in the era of internet, various online marketing sites and social networking sites are generating enormous amount of structural/semi structural data in the form of sales data, tweets, emails, web pages and so on. This online generated data is too large that it becomes very complex to process and analyze it using traditional systems which consumes more time. This paper overcomes the main memory bottleneck in single computing system. There are two major goals of this paper. In this paper, big sales dataset of AMUL dairy is preprocessed using Hadoop Map Reduce that convert it into the transactional dataset. Then, after removing the null transactions; distributed frequent pattern mining algorithm MR-DARM (Map Reduce based Distributed Association Rule Mining) is used to find most frequent item set. Finally, strong association rules are generated from frequent item sets. The paper also compares the time efficiency of MR-DARM algorithm with existing Count Distributed Algorithm (CDA) and Fast Distributed Mining (FDM) distributed frequent pattern mining algorithms. The compared algorithms are presented together with experimental results that lead to the final conclusions.
Scalable frequent itemset mining using heterogeneous computing par apriori a...ijdpsjournal
Association Rule mining is one of the dominant tasks of data mining, which concerns in finding frequent
itemsets in large volumes of data in order to produce summarized models of mined rules. These models are
extended to generate association rules in various applications such as e-commerce, bio-informatics,
associations between image contents and non image features, analysis of effectiveness of sales and retail
industry, etc. In the vast increasing databases, the major challenge is the frequent itemsets mining in a
very short period of time. In the case of increasing data, the time taken to process the data should be
almost constant. Since high performance computing has many processors, and many cores, consistent runtime
performance for such very large databases on association rules mining is achieved. We, therefore,
must rely on high performance parallel and/or distributed computing. In literature survey, we have studied
the sequential Apriori algorithms and identified the fundamental problems in sequential environment and
parallel environment. In our proposed ParApriori, we have proposed parallel algorithm for GPGPU, and
we have also done the results analysis of our GPU parallel algorithm. We find that proposed algorithm
improved the computing time, consistency in performance over the increasing load. The empirical analysis
of the algorithm also shows that efficiency and scalability is verified over the series of datasets
experimented on many core GPU platform.
Clustering is also known as data segmentation aims to partitions data set into groups, clusters, according to their similarity. Cluster analysis has been extensively studied in many researches. There are many algorithms for different types of clustering. These classical algorithms can't be applied on big data due to its distinct features. It is a challenge to apply the traditional techniques on large unstructured data. This study proposes a hybrid model to cluster big data using the famous traditional K-means clustering algorithm. The proposed model consists of three phases namely; Mapper phase, Clustering Phase and Reduce phase. The first phase uses map-reduce algorithm to split big data into small datasets. Whereas, the second phase implements the traditional clustering K-means algorithm on each of the spitted small data sets. The last phase is responsible of producing the general clusters output of the complete data set. Two functions, Mode and Fuzzy Gaussian, have been implemented and compared at the last phase to determine the most suitable one. The experimental study used four benchmark big data sets; Covtype, Covtype-2, Poker, and Poker-2. The results proved the efficiency of the proposed model in clustering big data using the traditional K-means algorithm. Also, the experiments show that the Fuzzy Gaussian function produces more accurate results than the traditional Mode function.
Scalable Rough C-Means clustering using Firefly algorithm..................................................................1
Abhilash Namdev and B.K. Tripathy
Significance of Embedded Systems to IoT................................................................................................. 15
P. R. S. M. Lakshmi, P. Lakshmi Narayanamma and K. Santhi Sri
Cognitive Abilities, Information Literacy Knowledge and Retrieval Skills of Undergraduates: A
Comparison of Public and Private Universities in Nigeria ........................................................................ 24
Janet O. Adekannbi and Testimony Morenike Oluwayinka
Risk Assessment in Constructing Horseshoe Vault Tunnels using Fuzzy Technique................................ 48
Erfan Shafaghat and Mostafa Yousefi Rad
Evaluating the Adoption of Deductive Database Technology in Augmenting Criminal Intelligence in
Zimbabwe: Case of Zimbabwe Republic Police......................................................................................... 68
Mahlangu Gilbert, Furusa Samuel Simbarashe, Chikonye Musafare and Mugoniwa Beauty
Analysis of Petrol Pumps Reachability in Anand District of Gujarat ....................................................... 77
Nidhi Arora
Frequent pattern mining techniques helpful to find interesting trends or patterns in
massive data. Prior domain knowledge leads to decide appropriate minimum support threshold. This
review article show different frequent pattern mining techniques based on apriori or FP-tree or user
define techniques under different computing environments like parallel, distributed or available data
mining tools, those helpful to determine interesting frequent patterns/itemsets with or without prior
domain knowledge. Proposed review article helps to develop efficient and scalable frequent pattern
mining techniques.
Evaluation of a New Incremental Classification Tree Algorithm for Mining High...mlaij
A new model for online machine learning process of high speed data stream is proposed, to minimize the severe restrictions associated with the existing computer learning algorithms. Most of the existing models have three principle steps. In the first step, the system would create a model incrementally. In the second step the time taken by the examples to complete a prescribed procedure with their arrival speed is computed. In the third and final step of the model the size of memory required for computation is predicted in advance. To overcome these restrictions we proposed this new data stream classification algorithm, where the data can be partitioned into stream of trees. In this algorithm, the new data set can be updated with the existing tree. This algorithm, called incremental classification tree algorithm, is proved to be an excellent solution for processing larger data streams. In this paper, we present the experimental results of our new algorithm and prove that our method would eradicate the problems of the existing method.
EVALUATION OF A NEW INCREMENTAL CLASSIFICATION TREE ALGORITHM FOR MINING HIGH...mlaij
Abstract—A new model for online machine learning process of high speed data stream is proposed, to
minimize the severe restrictions associated with the existing computer learning algorithms. Most of the
existing models have three principle steps. In the first step, the system would create a model incrementally.
In the second step the time taken by the examples to complete a prescribed procedure with their arrival
speed is computed. In the third and final step of the model the size of memory required for computation is
predicted in advance. To overcome these restrictions we proposed this new data stream classification
algorithm, where the data can be partitioned into stream of trees. In this algorithm, the new data set can be
updated with the existing tree. This algorithm, called incremental classification tree algorithm, is proved to
be an excellent solution for processing larger data streams. In this paper, we present the experimental
results of our new algorithm and prove that our method would eradicate the problems of the existing
method.
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.
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Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 3
FiDoop: Parallel Mining of Frequent Itemsets Using MapReduce
1. FiDoop: Parallel Mining of Frequent Itemsets Using
MapReduce
Dr G Krishna Kishore1
Suresh Babu Dasari2
Computer Science and Engineering Computer Science and Engineering
V. R. Siddhartha Engineering College V. R. Siddhartha Engineering College
Vijayawada, Andhra Pradesh, India Vijayawada, Andhra Pradesh, India
gkk@vrsiddhartha.ac.in dasarisuresh88@gmail.com
S. Ravi Kishan3
Computer Science & Engineering
V.R.Siddhartha Engineering College
Vijayawada, Andhra Pradesh
suraki@vrsiddhartha.ac.in
Abstract: Existing parallel digging calculations for
visit itemsets do not have a component that
empowers programmed parallelization, stack
adjusting, information conveyance, and adaptation
to non-critical failure on substantial bunches. As an
answer for this issue, we outline a parallel incessant
itemsets mining calculation called FiDoop utilizing
the MapReduce programming model. To
accomplish compacted capacity and abstain from
building contingent example bases, FiDoop joins
the incessant things Ultrametric tree, as opposed to
ordinary FP trees. In FiDoop, three MapReduce
occupations are actualized to finish the mining
undertaking. In the essential third MapReduce
work, the mappers autonomously disintegrate
itemsets, the reducers perform mix activities by
building little Ultrametric trees, and the genuine
mining of these trees independently. We actualize
FiDoop on our in-house Hadoop group. We
demonstrate that FiDoop on the group is touchy to
information dissemination and measurements, in
light of the fact that itemsets with various lengths
have diverse decay and development costs. To
enhance FiDoop's execution, we build up a
workload adjust metric to quantify stack adjust
over the group's registering hubs. We create
FiDoop-HD, an augmentation of FiDoop, to
accelerate the digging execution for high-
dimensional information investigation. Broad tests
utilizing genuine heavenly phantom information
exhibit that our proposed arrangement is productive
and versatile.
Keywords - MapReduce, Frequent Itemsets Mining,
Hadoop, Ultrametric, Celestial Spectral Data.
1. Introduction:
Visit Itemsets Mining (FIM) is a center issue in
affiliation run mining (ARM), succession mining,
and so forth. Accelerating the procedure of FIM is
basic and basic, on the grounds that FIM utilization
represents a critical segment of mining time
because of its high calculation and
information/yield (I/O) power. At the point when
datasets in present day information mining
applications turn out to be too much substantial,
successive FIM calculations running on a
singlemachine experience the ill effects of
execution disintegration. To address this issue, we
explore how to perform FIM utilizing MapReduce
a broadly embraced programming model for
handling huge datasets by misusing the parallelism
among registering hubs of a group. We
demonstrate to disseminate an extensive dataset
over the group to adjust stack over all bunch hubs,
in this manner enhancing the execution of parallel
FIM.
2. LITERATURE REVIEW
Data mining faces a lot of challenges in the big
data era. Association rule mining algorithm is not
sufficient to process large data sets. Apriori
algorithm has limitations like the high I/O load and
low performance. The FP-Growth algorithm also
has certain limitations like less internal memory.
Mining the frequent itemset in the dynamic
scenarios is a challenging task. A parallelized
approach using the MapReduce framework is also
used to process large data sets .The most efficient
the recent method is the FiDoop using Ultrametric
tree (FIUT) and MapReduce programming model.
FIUT scans the database only twice. FIUT has four
advantages. First: I reduces the I/O overhead as it
scans the database only twice. Second: only
frequent itemsets in each transaction are inserted as
nodes for compressed storage. Third: FIU is
improved way to partition database, which
International Journal of Computer Science and Information Security (IJCSIS),
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153 https://sites.google.com/site/ijcsis/
ISSN 1947-5500
2. significantly reduces the search space. Fourth:
frequent itemsets are generated by checking only
leaves of tree rather than traversing entire tree,
which reduces the computing time. The mining of
frequent itemsets is a basic and essential work in
many data mining applications. Frequent itemsets
extraction with frequent pattern and rules boosts
the applications like Association rule mining, co-
relations also in product sale and marketing. In
extraction process of frequent itemsets there are
number of algorithms used like FP-growth, E-clat
etc. But unfortunately these algorithms are
inefficient in distributing and balancing the load,
when it comes across massive data. Automatic
parallelization is also not possible with these
algorithms. To defeat these issues of existing
algorithms there is need to construct an algorithm
which will support the missing features, such as
automatically parallelization, balancing and good
distribution of data. This paper is focusing on an
efficient methodology to extract frequent itemsets
with the popular MapReduce approach. This new
methodology consist an algorithm which is build
using Modified Apriori algorithm, called as
Frequent Itemset Mining using Modified Apriori
(FIMMA) Technique. This methodology works
with three mappers, independently and
concurrently by using the decompose strategy. The
result of these mappers will be given to the
reducers using the hash table method. Reducer
gives the top most frequent itemsets.
3. Proposed System
In Proposed System a new data partitioning method
to well balance computing load among the cluster
nodes; we develop FiDoop-HD, an extension of
FiDoop, to meet the needs of high dimensional data
processing.
Step 1: Count the occurrence of each item.
Figure 3.1:Frequency of each item
Step 2: We start making pairs out of the
frequent itemsets we got in the above step.
Figure 3.2:Frequent item sets pairs.
Step 3: After getting the frequent Item Pairs, we
start counting the occurrence of these pairs in the
Transaction Set.
Figure 3.3:Frequency of itemset pairs
Step 4: Make combinations of triples using the
frequent Item pairs.
To make triples, the rule is: IF 12 and 13 are
frequent, then the triple would be 123. Similarly, if
24 and 26 then triple would be 246.
So, using the above logic and our Frequent Item
Pairs table, we get the below triples:
Figure 3.4:Frequent itemset triplets.
International Journal of Computer Science and Information Security (IJCSIS),
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3. Step 5: Get the count of the above triples
(Candidates).
Figure 3.5:Frequency of itemsets triplets.
After, this, if we can find quartets, then we find
those and count their occurrence/frequency.
If we had 123, 124, 134, 135, 234 and we wanted
to generate a quartet then it would be 1234 and
1345. And after finding quartet we would have
again got their count of occurrence /frequency and
repeated the same also, until the Frequent ItemSet
is null.
Thus, the frequent ItemSets are:
- Frequent Itemsets of Size 1: 1, 2, 4, 5, 6
- Frequent Itemsets of Size 2: 14, 24, 25, 45, 46
- Frequent Itemsets of Size 3: 245
3.1 METHODOLOGY
In Proposed System a new data partitioning method
to well balance computing load among the cluster
nodes; we develop FiDoop-HD, an extension of
FiDoop, to meet the needs of high dimensional data
processing. FiDoop is efficient and scalable on
Hadoop clusters.
The proposed system involves the following steps:
Load the data base into the system.
Perform mining on all datasets of the
database.
Calculate the support values and
confidence values of the datasets.
Sort the elements based on their support
values.
Set the threshold support value.
Extract the elements with support values
above threshold.
Approach
1) Finding the Frequent Items: During the
first step, the vertical database is divided
into equally sized blocks (shards) and
distributed to available mappers. Each
mapper extracts the frequent singletons
from its shard. In the reduce phase, all
frequent items are gathered without
further processing.
2) k-FIs Generation: In this second step, Pk,
the set of frequent itemsets of size k, is
generated. First, frequent singletons are
distributed across m mappers. Each of the
mappers finds the frequent k-sized
supersets of the items by running Eclat to
level k. Finally, a reducer assigns Pk to a
new batch of m mappers. Distribution is
done using Round-Robin.
3) Subtree Mining: The last step consists of
mining the prefix tree starting at a prefix
from the assigned batch using Eclat. Each
mapper can complete this step
independently since sub-trees do not
require mutual information.
Figure 3.1.1 Map Reduceprocess
4. IMPLEMENTATION:
Data set: Groceries data set in csv format.
INPUT: Transactions dataset i.e groceries dataset.
OUTPUT: Frequent itemsets
There are three modules in the proposed system.
They are as follows:
MODULE 1:
International Journal of Computer Science and Information Security (IJCSIS),
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4. The first mapper program would mine the
transaction database by removing infrequent sets.
This output from the map is given to reducer as an
input which would order the frequent itemsets in
descending order and would build a FP tree.
Algorithm:
Input: minsupport, DBi;
Output: FP tree
1. function MAP(key offset, values DBi)
2. //T is the transaction in DBi
3. for all T do
4. items ←split each T;
5. for all item in items do 1. count++ 2. end for
6. output( item, count);
7. end for
8. end function
10. reduce input: (itemset, count )
11. function REDUCE(key item, values count)
12. Items=sort(itemset, count) /*sorts the items in
descending order*/
13. fptree_generation(items); /*generates FP tree */
14. end function
MODULE 2:
The second map - reducer program takes the output
from the second reducer , which would recursively
processes the data and generates a minimum 2 Item
sets using the FiDoopHD algorithm.
Algorithm:
Input: List,
Output:-FP Tree
1. function MAP(List)
2. // M is the size of the List 2. for all (k is from M
to 2) do
3. for all (k-itemset in List) do
4. decompose(k-itemset, k-1, (k-1)-itemsets);
/*Each k-itemset is only decomposed into (k-1)-
itemsets */
5. (k-1)-file ← the decomposed (k-1)-itemsets
6. union the original (k-1)-itemsets in (k-1)-file; 2.
for all (t-itemset in (k-1)-file) do 3. t -FP-tree←t-
FP-tree generation(local-FPtree,t itemset);
8. output(t, t-FP-tree);
9. end for
10. end for
11. end for
12. end function
5. OUTPUT:
The following diagrams shows the implementation
of Fidoop and display of frequent itemsets for the
given datasets.
Figure 5.1 Execution of Fidoop
. Figure 5.2: Generation of Output File and
Success File
International Journal of Computer Science and Information Security (IJCSIS),
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ISSN 1947-5500
5. Figure 5.3: Display of Frequent Item Sets
6. CONCLUSION AND FUTURE WORK
To mitigate high communication and reduce
computing cost in MapReduce-based FIM
algorithms, we developed FiDoop-DP, which
exploits correlation among transactions to partition
a large dataset across data nodes in a Hadoop
cluster. FiDoop-DP is able to partition transactions
with high similarity together and group highly
correlated frequent items into a list.
7. REFERENCES
1) Shreedevi C Patil “A Survey on Parallel
Mining of frequent Itemsets in
MapReduce”, International Journal of
Innovative Research in Computer and
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Engineering - a practitioner's approach”,
Fifth Edition.
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Definitive Guide”, Third Edition.
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ISSN 1947-5500