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A Rough Set Theoretic Approach to Clustering

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Wybrane pełne teksty z tego czasopisma
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Języki publikacji
EN
Abstrakty
EN
In this paper, cluster analysis based on fuzzy relations is investigated. This paper starts with fuzzy proximity relations and cluster analysis based on fuzzy proximity relation is performed by using rough approximation. A clustering algorithm has been proposed and explained with an example.
Wydawca
Rocznik
Strony
409--417
Opis fizyczny
Bibliogr. 29 poz.
Twórcy
autor
  • Operations Management and Information Systems Area, XLRl Jamshedpur, Jamshedpur-831001, INDIA, skde@xlri.ac.in
Bibliografia
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  • [7] Shoji Hirano and Shusaku Tsumoto, An Indiscemibility-Based Clustering Method With Iterative Retinement of Equivalence Relations, In proceedings of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), Cavtat-Dubrovnik, Croatia, September 22-26, 2003; Lecture Notes in Computer Science, Springer-Verlag Heidelberg, 2838 (2003), 192-203.
  • [8] Shoji Hirano and Shusaku Tsumoto, Indiscemibility-Based Clustering : Rough Clustering, IFSA 2003, Lecture Notes in Computer Science, Springer-Verlag Heidelberg, 2715 (2003), 378-386.
  • [9] Marzena Kryszkiewicz, Rough Set Approach to Incomplete Information Systems, Information Sciences 112(1-4), (1998), 39-49.
  • [10] Pawan Lingras, R.Yan and A.Jain, Web Usage Mining: Comparison of Conventional, Fuzzy, and Rough Set Clustering, to appear in: Y. Zhang and Y. Yao (Eds.), Computational Web Intelligence: Intelligent Technology for Web Applicatios.
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  • [13] Sankar K. Pal and Pabitra Mitra, Case Generation Using Rough Sets With Fuzzy Representation, IEEE Trans, on Knowledge and Data Engineering, 16(3), 2004, 292-300.
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS2-0005-0088
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