Fuzzy K-mean clustering in MapReduce on cloud based hadoop

Clustering is regarded as one of the significant task in data mining which deals with primarily grouping of similar data. To cluster large data is a point of concern. Hadoop is a software framework which deals with distributed processing of huge amount of data across clusters of commodity computers using MapReduce programming model. MapReduce allows a kind of parallelization for solving a problem involving large data sets using computing clusters and is also an attractive mean for data clustering involving large datasets. Mahout, a scalable machine learning library is an approach to Fuzzy K-mean clustering which runs on a Hadoop. This paper focuses on studying the performance of different datasets using Fuzzy K-mean clustering in MapReduce on Hadoop. Experimental results depict the execution time of the approach on a multi-node Hadoop cluster which is build using Amazon Elastic Cloud Computing(Amazon EC2).