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Scalable Clustering of Documents with Multiple Membership

By Jack Newton and Chris O&apos


Document clustering has recently garnered a large amount of attention from the IR, data mining, and machine learning research communities as an effective way of not only organizing textual information, but also for discovering interesting patterns in that information. Most existing methods, however, suffer from two main drawbacks. First, most clustering algorithms are very restrictive, as documents are only allowed to participate in a single cluster. Allowing documents to participate in more than one cluster is important in the context of document clustering, since a document can often span more than two topics or concepts. Second, most existing methods cannot scale to very large document collections

Year: 2007
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