Sequential cluster extraction using power-regularized possibilistic c-means

研究成果: Article

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The present study proposes an algorithm for sequential cluster extraction using power-regularized possibilistic c-means (pPCM). First, pPCM is derived in a similar manner to two types of entropy-regularized possibilistic c-means (ePCM) derivations, where a power function is utilized instead of the negative entropy in ePCM. The cluster fusion with pPCM is identical to the mean-shift with a generalized Epanichnikov kernel, whereas the proposed method employs sequential cluster extraction with pPCM. Numerical examples show that the cluster number produced by the proposed algorithm did not match with the true class number in real datasets, but the extracted clustering results were partially successful in terms of capturing dense regions of objects.

元の言語English
ページ(範囲)67-73
ページ数7
ジャーナルJournal of Advanced Computational Intelligence and Intelligent Informatics
19
発行部数1
DOI
出版物ステータスPublished - 2015 1 1

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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