Semi-supervised fuzzy c-means algorithms by revising dissimilarity/kernel matrices

研究成果: Chapter

抄録

Semi-supervised clustering uses partially labeled data, as often occurs in practical clustering, to obtain a better clustering result. One approach uses hard constraints which specify data that must and cannot be within the same cluster. In this chapter, we propose another approach to semi-supervised clustering with soft pairwise constraints. The clustering method used is fuzzy c-means (FCM), a commonly used fuzzy clustering method. Two previously proposed variants, entropy- regularized relational/kernel fuzzy c-means clustering and indefinite kernel fuzzy c-means clustering algorithm are modified to use the soft constraints. In addition, a method is discussed that propagates pairwise constraints when the given constraints are not sufficient for obtaining the desired clustering result. Using some numerical examples, it is shown that the proposed algorithms obtain better clustering results.

本文言語English
ホスト出版物のタイトルStudies in Computational Intelligence
出版社Springer Verlag
ページ45-61
ページ数17
671
DOI
出版ステータスPublished - 2017 1 1

出版物シリーズ

名前Studies in Computational Intelligence
671
ISSN(印刷版)1860949X

ASJC Scopus subject areas

  • 人工知能

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