Unsupervised optimal fuzzy clustering

Author

Gath, I. ; Geva, A.B.

Author_Institution

Technion-Israel Inst. of Technol., Haifa, Israel

Volume

11

Issue

7

fYear

1989

fDate

7/1/1989 12:00:00 AM

Firstpage

773

Lastpage

780

Abstract

This study reports on a method for carrying out fuzzy classification without a priori assumptions on the number of clusters in the data set. Assessment of cluster validity is based on performance measures using hypervolume and density criteria. An algorithm is derived from a combination of the fuzzy K-means algorithm and fuzzy maximum-likelihood estimation. The unsupervised fuzzy partition-optimal number of classes algorithm performs well in situations of large variability of cluster shapes, densities, and number of data points in each cluster. The algorithm was tested on different classes of simulated data, and on a real data set derived from sleep EEG signal

Keywords

electroencephalography; fuzzy set theory; pattern recognition; cluster validity; fuzzy K-means algorithm; fuzzy classification; fuzzy maximum-likelihood estimation; fuzzy set theory; pattern recognition; sleep EEG signal; unsupervised fuzzy partition-optimal number of classes algorithm; unsupervised optimal fuzzy clustering; Brain modeling; Clustering algorithms; Density measurement; Electroencephalography; Fuzzy sets; Maximum likelihood estimation; Partitioning algorithms; Shape; Sleep; Testing;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/34.192473

Filename

192473