A Framework for Unsupervised Selection of Indiscernibility Threshold in Rough Clustering

  • Shoji Hirano
  • Shusaku Tsumoto
Conference paper

DOI: 10.1007/11908029_90

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4259)
Cite this paper as:
Hirano S., Tsumoto S. (2006) A Framework for Unsupervised Selection of Indiscernibility Threshold in Rough Clustering. In: Greco S. et al. (eds) Rough Sets and Current Trends in Computing. RSCTC 2006. Lecture Notes in Computer Science, vol 4259. Springer, Berlin, Heidelberg

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.

Keywords

Indiscernibility Clustering Rough Sets 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shoji Hirano
    • 1
  • Shusaku Tsumoto
    • 1
  1. 1.Department of Medical InformaticsShimane University, School of MedicineShimaneJapan

Personalised recommendations