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Optimal Clustering with Twofold Memberships

  • Sadaaki Miyamoto
  • Jong Moon Choi
  • Yasunori Endo
  • Van Nam Huynh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11144)

Abstract

This paper proposes two clustering algorithms of twofold memberships for each cluster. One uses a membership similar to that in K-means, while another membership is defined for a core of a cluster, which is compared to the lower approximation of a cluster in rough K-means. Two ideas for the lower approximation are proposed in this paper: one uses a neighborhood of a cluster boundary and another uses a simple circle from a cluster center. By using the two memberships, two alternate optimization algorithms are proposed. Numerical examples show the effectiveness of the proposed algorithms.

Keywords

Neighborhood Clustering K-means Rough K-means Twofold memberships 

Notes

Acknowledgment

This paper is based upon work supported in part by the Air Force Office of Scientific Research/Asian Office of Aerospace Research and Development (AFOSR/AOARD) under award number FA2386-17-1-4046.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Sadaaki Miyamoto
    • 1
  • Jong Moon Choi
    • 1
  • Yasunori Endo
    • 1
  • Van Nam Huynh
    • 2
  1. 1.University of TsukubaTsukubaJapan
  2. 2.Japan Advanced Institute of Science and TechnologyNomiJapan

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