Implementing K-Means Clustering Algorithm Using MapReduce Paradigm

Clustering is a useful data mining technique which groups data points such that the points within a single group have similar characteristics, while the points in different groups are dissimilar. Partitioning algorithm methods such as k-means algorithm is one kind of widely used clustering algorithms. As there is an increasing trend of applications to deal with vast amounts of data, clustering such big data is a challenging problem. Recently, partitioning clustering algorithms on a large cluster of commodity machines using the MapReduce framework have received a lot of attention. Traditional way of clustering text documents is Vector space model, in which tf-idf is used for k-means algorithm with supportive similarity measure. This project exhibits an approach to cluster text documents in which results obtained by executing map reduce k-means algorithm on single node cluster show that the performance of the algorithm increases as the text corpus increases.