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...
Saved in:
Main Author: | |
---|---|
Format: | Thesis |
Published: |
2012
|
Subjects: | |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Summary: | 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. |
---|