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WIREs Data Mining Knowl Discov
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Soft clustering for information retrieval applications

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Abstract This paper overviews soft clustering algorithms applied in the context of information retrieval (IR). First, a motivation of the utility of soft clustering approaches in IR is discussed. Then, an outline of the two main flat soft approaches, namely probabilistic clustering and fuzzy clustering, is described. Specifically, the expectation maximization and fuzzy c‐means algorithms are introduced, and some of their extensions defined to overcome their main drawbacks when applied for organizing large document collections. Finally, soft hierarchical clustering algorithms designed for generating taxonomies of documents are introduced. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 138‐146 DOI: 10.1002/widm.3 This article is categorized under: Algorithmic Development > Hierarchies and Trees Fundamental Concepts of Data and Knowledge > Information Repositories Technologies > Computational Intelligence Technologies > Structure Discovery and Clustering

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Algorithmic Development > Hierarchies and Trees
Fundamental Concepts of Data and Knowledge > Information Repositories
Technologies > Structure Discovery and Clustering
Technologies > Computational Intelligence

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