Some Efficient and Fast Approaches to Document Clustering

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Author(s): P. Viswanth (Indian Institute of Technology Guwahati, India)
Copyright: 2009
Pages: 8
Source title: Handbook of Research on Text and Web Mining Technologies
Source Author(s)/Editor(s): Min Song (New Jersey Institute of Technology, USA)and Yi-Fang Brook Wu (New Jersey Institute of Technology, USA)
DOI: 10.4018/978-1-59904-990-8.ch011

Keywords: Data Mining / Data Mining and Databases / Information Science Reference / Library & Information Science

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Abstract

Clustering is a process of finding natural grouping present in a dataset. Various clustering methods are proposed to work with various types of data. The quality of the solution as well as the time taken to derive the solution is important when dealing with large datasets like that in a typical documents database. Recently hybrid and ensemble based clustering methods are shown to yield better results than conventional methods. The chapter proposes two clustering methods; one is based on a hybrid scheme and the other based on an ensemble scheme. Both of these are experimentally verified and are shown to yield better and faster results.

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