Cluster-Based Text Categorization: A Comparison of Category Search Strategies.

Makoto Iwayama, Takenobu Tokunaga: Cluster-Based Text Categorization: A Comparison of Category Search Strategies. SIGIR 1995: 273-280

@inproceedings{DBLP:conf/sigir/IwayamaT95,
  author    = {Makoto Iwayama and
               Takenobu Tokunaga},
  editor    = {Edward A. Fox and
               Peter Ingwersen and
               Raya Fidel},
  title     = {Cluster-Based Text Categorization: A Comparison of Category Search
               Strategies},
  booktitle = {SIGIR'95, Proceedings of the 18th Annual International ACM SIGIR
               Conference on Research and Development in Information Retrieval.
                Seattle, Washington, USA, July 9-13, 1995 (Special Issue of
               the SIGIR Forum)},
  publisher = {ACM Press},
  year      = {1995},
  isbn      = {0-89791-714-6},
  pages     = {273-280},
  ee        = {db/conf/sigir/IwayamaT95.html},
  crossref  = {DBLP:conf/sigir/95},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

Text categorization can be viewed as a process of category search, in which one or more categories for atest document are searched for by using given training documents with known categories. In this paper a cluster-based search with a probabilistic clustering algorithm is proposed and evaluated on two data sets. The efficiency, effectiveness, and noise tolerance of this search strategy were confirmed to be better than those of a full search, a category-based search, and a cluster-based search with nonprobabilistic clustering.

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Printed Edition

Edward A. Fox, Peter Ingwersen, Raya Fidel (Eds.): SIGIR'95, Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Seattle, Washington, USA, July 9-13, 1995 (Special Issue of the SIGIR Forum). ACM Press 1995, ISBN 0-89791-714-6
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