Semi-supervised Fuzzy <i>c</i>-Means Clustering for Data with Clusterwise Tolerance with Pairwise Constraints

• Hamasuna Yukihiro

  Research Fellow of the Japan Society for the Promotion of Science University of Tsukuba

• Endo Yasunori

  University of Tsukuba

Abstract

Recently, semi-supervised clustering has been remarked and discussed in many research fields. In semi-supervised clustering, pairwise constraints such as must-link and cannot-link are often introduced to improve clustering results or properties. In this paper, we will propose a new semi-supervised fuzzy c-means clustering for data with clusterwise tolerance with pairwise constraints. First, the concept of clusterwise tolerance and pairwise constraints are introduced. Second, the optimization problem of fuzzy c-means clustering for data with clusterwise tolerance with pairwise constraints is formulated. Third, a new clustering algorithm is constructed based on the above mathematical discussions. Finally, the effectiveness of proposed algorithm is verified through numerical examples.

Journal

• SCIS & ISIS

  SCIS & ISIS 2010 (0), 397-400, 2010

  Japan Society for Fuzzy Theory and Intelligent Informatics

Keywords

• Semi-supervised Clustering
• Clusterwise Tolerance
• Pairwise Constraints

Details

• CRID: 1390282680566670080
• NII Article ID: 130005019594
• DOI: 10.14864/softscis.2010.0.397.0
• Text Lang: en
• Data Source:
  • JaLC
  • CiNii Articles
• Abstract License Flag: Disallowed