Citation

Nejad, Elaheh Mahraban (2012) Weka - Hadoop Data Mining Techniques and Applications. Masters thesis, Multimedia University.

Full text not available from this repository.

Abstract

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.

Item Type:	Thesis (Masters)
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science > QA76.75-76.765 Computer software
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Users 1102 not found.
Date Deposited:	23 Nov 2012 01:42
Last Modified:	23 Nov 2012 01:42
URII:	http://shdl.mmu.edu.my/id/eprint/3635

Downloads

Edit (login required)