期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2018
卷号：6
期号：5
页码：5613-5618
DOI：10.15680/IJIRCCE.2018.0605023
出版社：S&S Publications
摘要：Clustering is a useful data mining technique which group’s 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 scheme or paper exhibits an approach to cluster text documents in
which results obtained by executing map reduce k-means algorithm on single node cluster on hadoop show that the
performance of the algorithm increases as the text corpus increases thus forming the non-redundant results and
appropriate information.
关键词：Big Data; Hadoop; Yet Another Resource Negotiator (YARN); K;Means
