Weka - Hadoop Data Mining Techniques and Applications

Weka - Hadoop Data Mining Techniques and Applications

Weka-Hadoop techniques are considered for data mining applications in this project. The aim of this research is to detect financial frauds by applying Apriori Algorithm and clustering techniques in bulk of dataset that are generated from finance transactions. This process may be computed in centrali...

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Main Author: Nejad, Elaheh Mahraban
Format: Thesis
Published: 2012
Subjects:
QA76.75-76.765 Computer software
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id my-mmu-ep.3635
record_format uketd_dc
spelling my-mmu-ep.36352012-11-23T01:42:15Z Weka - Hadoop Data Mining Techniques and Applications 2012-05 Nejad, Elaheh Mahraban QA76.75-76.765 Computer software Weka-Hadoop techniques are considered for data mining applications in this project. The aim of this research is to detect financial frauds by applying Apriori Algorithm and clustering techniques in bulk of dataset that are generated from finance transactions. This process may be computed in centralized and distributed environment. Weka provides centralized platform for data mining applications. Hadoop distributed file system and MapReduce programming model are considered as the methodology for distributed datamining in Hadoop-Mahout for finding association rules/patterns algorithm and clustering. Hadoop-Mahout provides a platform for distributed computing for implementing many machine learning and data mining algorithms. 2012-05 Thesis http://shdl.mmu.edu.my/3635/ http://vlib.mmu.edu.my/diglib/login/dlusr/login.php masters Multimedia University Faculty of Information Technology
institution Multimedia University
collection MMU Institutional Repository
topic QA76.75-76.765 Computer software
spellingShingle QA76.75-76.765 Computer software
Nejad, Elaheh Mahraban
Weka - Hadoop Data Mining Techniques and Applications
description Weka-Hadoop techniques are considered for data mining applications in this project. The aim of this research is to detect financial frauds by applying Apriori Algorithm and clustering techniques in bulk of dataset that are generated from finance transactions. This process may be computed in centralized and distributed environment. Weka provides centralized platform for data mining applications. Hadoop distributed file system and MapReduce programming model are considered as the methodology for distributed datamining in Hadoop-Mahout for finding association rules/patterns algorithm and clustering. Hadoop-Mahout provides a platform for distributed computing for implementing many machine learning and data mining algorithms.
format Thesis
qualification_level Master's degree
author Nejad, Elaheh Mahraban
author_facet Nejad, Elaheh Mahraban
author_sort Nejad, Elaheh Mahraban
title Weka - Hadoop Data Mining Techniques and Applications
title_short Weka - Hadoop Data Mining Techniques and Applications
title_full Weka - Hadoop Data Mining Techniques and Applications
title_fullStr Weka - Hadoop Data Mining Techniques and Applications
title_full_unstemmed Weka - Hadoop Data Mining Techniques and Applications
title_sort weka - hadoop data mining techniques and applications
granting_institution Multimedia University
granting_department Faculty of Information Technology
publishDate 2012
_version_ 1747829531559854080

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