Unsupervised learning algorithm for fuzzy clustering.

Kiichi Urahama

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


An adaptive algorithm is presented for fuzzy clustering of data. Partitioning is fuzzified by addition of an entropy term to objective functions. The proposed method produces more convex membership functions than those given by the fuzzy c- means algorithm.

Original languageEnglish
Pages (from-to)390-391
Number of pages2
JournalIEICE Transactions on Information and Systems
Issue number3
Publication statusPublished - Mar 1993
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence


Dive into the research topics of 'Unsupervised learning algorithm for fuzzy clustering.'. Together they form a unique fingerprint.

Cite this