Study and Analysis on Document Clustering Based on MapReduce in Hadoop using K-Mean Algorithm

Yashika Verma, Sumit Kumari

Abstract: Document clustering is an effective tool to manage information overload. By grouping similar documents together, we enable a human observer to quickly browse large document collections, make it possible to easily grasp the distinct topics and subtopics in them, allow search engines to efficiently query large document collections among many other applications. Hence, it has been widely studied as a part of the broad literature of data clustering. MapReduce is a simplified programming model of distributed parallel computing. It is an important technology of Google, and is commonly used for data-intensive distributed parallel computing. In this paper, we describe how document clustering for large collection can be efficiently implemented with MapReduce. Hadoop implementation provides a convenient and flexible framework for distributed computing on cluster of commodity machines. The design and implementation of direct K-Means and Distributed K-means algorithm on MapReduce is presented.

Keywords: Hadoop, Mapreduce, Document Clustering, Direct K-Means, Distributed K-Means, Large DataSet

How to Cite?: Yashika Verma, Sumit Kumari, "Study and Analysis on Document Clustering Based on MapReduce in Hadoop using K-Mean Algorithm", Volume 4 Issue 8, August 2015, International Journal of Science and Research (IJSR), Pages: 176-180, https://www.ijsr.net/getabstract.php?paperid=SUB157223, DOI: https://dx.doi.org/10.21275/SUB157223