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A Critical Review of K Means Text Clustering Algorithms

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Author(s): Francis Musembi Kwale

Journal: International Journal of Advanced Research in Computer Science
ISSN 0976-5697

Volume: 04;
Issue: 09;
Start page: 27;
Date: 2013;
Original page

Keywords: text | mining | text | clustering | clusters | and | K | Means.

ABSTRACT
Text clustering is a text mining technique used to group text documents into groups (or clusters) based on similarity of content. This organization (i.e. clustering) is so as to make documents more understandable and easier to search the relevant information, easier to process, and even more efficient in utilizing communication bandwidth and storage space. An example is clustering results of a web search engine operation into groups of similar documents. Many text clustering algorithms have been developed using different approaches, but none can be said to be the best. The choice of a particular algorithm is a big issue to text clustering system developers. In this paper, we discuss the K Means algorithm as well as its variants. We describe the text representation used, the characteristics of the algorithms accompanied by some examples/illustrations, as well as their strengths and weaknesses. The paper thus gives an in depth view of the K Means algorithms, evaluates the algorithms, and also gives guidance to researchers of text mining concerning the choice of K Means for text clustering.
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