Obviously there are many ways to dene clusters and

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Unformatted text preview: ield excellent results. Hofmann (2001) formulates a variant that is able to cluster terms occurring together instead of documents. 3.2.3 Alternative Clustering Approaches algorithm designate the simultaneous clustering of documents and terms (Dhillon et al. 2003). They follow thereby another paradigm than the "classical" cluster algorithm as KMeans which only clusters elements of the Co-clustering 44 LDV-FORUM A Brief Survey of Text Mining one dimension on the basis of their similarity to the second one, e.g. documents based on terms. While most classical clustering algorithms assign each datum to exactly one cluster, thus forming a crisp partition of the given data, fuzzy clustering allows for degrees of membership, to which a datum belongs to different clusters (Bezdek 1981). These approaches are frequently more stable. Applications to text are described in, e.g., Mendes & Sacks (2001); Borgelt & Nürnberger (2004). Fuzzy Clustering We have described the most important types of clusteri...
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This note was uploaded on 06/19/2011 for the course IT 2258 taught by Professor Aymenali during the Summer '11 term at Abu Dhabi University.

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