Implementing K-Means Clustering Algorithm Using MapReduce Paradigm

Botcha Chandrasekhara Rao, Medara Rambabu

Abstract: 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.

Keywords: Vector space model, map reduce, text clustering, map reduce k-means, Hadoop

How to Cite?: Botcha Chandrasekhara Rao, Medara Rambabu, "Implementing K-Means Clustering Algorithm Using MapReduce Paradigm", Volume 5 Issue 7, July 2016, International Journal of Science and Research (IJSR), Pages: 1240-1244, https://www.ijsr.net/getabstract.php?paperid=14071601, DOI: https://dx.doi.org/10.21275/14071601

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