A Critical Review of K Means Text Clustering Algorithms
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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. K Means is arguably the most popular text clustering algorithm. However, just like the others, it must be having its own weaknesses. In this paper, we explore the K Means algorithm as well as its variants and discuss their appropriateness in text clustering. We describe the characteristics of the algorithms accompanied by some examples and illustrations in an attempt to discover the strengths and weaknesses. The paper thus gives an in depth view of the K Means algorithms, discusses the appropriateness of the algorithms, and also gives guidance to researchers of text mining concerning the choice of K Means for text clustering.
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Keywords: text mining, text clustering, clusters, and K Means.
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