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A Framework for Unsupervised Selection of Indiscernibility Threshold in Rough Clustering

  • Conference paper
Rough Sets and Current Trends in Computing (RSCTC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4259))

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Abstract

Indiscernibility threshold is a parameter in rough clustering that controls the global ability of equivalence relations for discriminating objects. During its second step, rough clustering iteratively refines equivalence relations so that the coarseness of classification of objects meets the given level of indiscernibility. However, as the relationships between this parameter and resultant clusters have not been studied yet, users should determine its value by trial and error. In this paper, we discuss the relationships between the threshold value of indiscernibility degree and clustering results, as a framework for automatic determination of indiscernibility threshold. The results showed that the relationships between indiscernibility degree and the number of clusters draw a globally convex but multi-modal curve, and the range of indiscernibility degree that yields best cluster validity may exist on a local minimum around the global one which generates single cluster.

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Author information

Authors and Affiliations

  1. Department of Medical Informatics, Shimane University, School of Medicine, 89-1 Enya-cho, Izumo, Shimane, 693-8501, Japan

    Shoji Hirano & Shusaku Tsumoto

Authors
  1. Shoji Hirano
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  2. Shusaku Tsumoto
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Editor information

Editors and Affiliations

  1. Faculty of Economics, University of Catania, Corso Italia, 55, 95129, Catania, Italy

    Salvatore Greco

  2. Graduate School of Engineering, Department of Electrical Engineering and Computer Sciences, University of Hyogo, 2167 Shosha, 671-2280,, Himeji, Hyogo, Japan

    Yutaka Hata

  3. Department of Medical Informatics, Faculty of Medicine, Shimane University, 89-1 Enya-cho, Izumo, 693-8501, Shimane, Japan

    Shoji Hirano

  4. Department of Systems Innovation, Graduate School of Engineering Science, Osaka University, 1-3, Machikaneyama, Toyonaka, 560-8531, Osaka, Japan

    Masahiro Inuiguchi

  5. Department of Risk Engineering, School of Systems and Information Engineering, University of Tsukuba, 305-8573, Ibaraki, Japan

    Sadaaki Miyamoto

  6. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Hung Son Nguyen

  7. Systems Research Institute, Polish Academy of Sciences, 01-447, Warsaw, Poland

    Roman Słowiński

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© 2006 Springer-Verlag Berlin Heidelberg

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Hirano, S., Tsumoto, S. (2006). A Framework for Unsupervised Selection of Indiscernibility Threshold in Rough Clustering. In: Greco, S., et al. Rough Sets and Current Trends in Computing. RSCTC 2006. Lecture Notes in Computer Science(), vol 4259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11908029_90

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  • DOI: https://doi.org/10.1007/11908029_90

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-47693-1

  • Online ISBN: 978-3-540-49842-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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