Fuzzy clustering with volume prototypes and adaptive cluster merging

U. Kaymak, M. Setnes

Research output: Contribution to journalArticleAcademicpeer-review

120 Citations (Scopus)
1 Downloads (Pure)

Abstract

Two extensions to objective function-based fuzzy clustering are proposed. First, the (point) prototypes are extended to hypervolumes, whose size can be fixed or can be determined automatically from the data being clustered. It is shown that clustering with hypervolume prototypes can be formulated as the minimization of an objective function. Second, a heuristic cluster merging step is introduced where the similarity among the clusters is assessed during optimization. Starting with an overestimation of the number of clusters in the data, similar clusters are merged in order to obtain a suitable partitioning. An adaptive threshold for merging is proposed. The extensions proposed are applied to Gustafson-Kessel and fuzzy c-means algorithms, and the resulting extended algorithm is given. The properties of the new algorithm are illustrated by various examples.
Original languageEnglish
Pages (from-to)705-712
JournalIEEE Transactions on Fuzzy Systems
Volume10
Issue number6
DOIs
Publication statusPublished - 2002

Fingerprint

Dive into the research topics of 'Fuzzy clustering with volume prototypes and adaptive cluster merging'. Together they form a unique fingerprint.

Cite this