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Frequent term-based text clustering
2002
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '02
Text clustering methods can be used to structure large sets of text or hypertext documents. The well-known methods of text clustering, however, do not really address the special problems of text clustering: very high dimensionality of the data, very large size of the databases and understandability of the cluster description. In this paper, we introduce a novel approach which uses frequent item (term) sets for text clustering. Such frequent sets can be efficiently discovered using algorithms
doi:10.1145/775047.775110
dblp:conf/kdd/BeilEX02
fatcat:cnkrq6xvwvao5ncwqqksgh6mse