Semantics based Distributed Document Clustering

Abstract

Document clustering, one of the traditional data mining techniques, is an unsupervised learning paradigm where clustering methods try to identify inherent groupings of the text newlinedocuments. It produces a set of clusters that exhibit high intra-cluster similarity and low intercluster similarity. Document clustering provides efficient representation and visualization of newlinethe documents which helps in easy navigation. It has been studied intensively because of its wide applicability in various areas such as web mining, search engines, recommendation newlinesystem, business, biology, information retrieval, etc. The importance of document clustering emerges from the massive volume of textual documents created. Although numerous document clustering methods have been extensively studied in past years, there still exist several challenges (like high dimensionality,scalability,semantics, efficiency, etc.) for increasing the clustering quality. Particularly, most of the current documents clustering newlinealgorithms do not consider the semantic relationships which produce unsatisfactory clustering results. Recently, the efforts have been in applying semantics to document clustering. newline