A fuzzy multistage evolutionary (FUME) clustering technique

doi:10.1016/0167-8655(84)90037-0

Pattern Recognition Letters

Volume 2, Issue 3, March 1984, Pages 139-145

https://doi.org/10.1016/0167-8655(84)90037-0 Get rights and content

Abstract

In this paper, a multistage evolutionary scheme is proposed for clustering in a large data base, like speech data. This is achieved by clustering a small subset of the entire sample set in each stage and treating the cluster centroids so obtained as samples, together with another subset of samples not considered previously, as input data to the next stage. This is continued till the whole sample set is exhausted. The clustering is accomplished by constructing a fuzzy similarity matrix and using the fuzzy techniques proposed here. The technique is illustrated by an efficient scheme for voiced-unvoiced-silence classification of speech.

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A fuzzy multistage evolutionary (FUME) clustering technique

Abstract

Inform. Control

Pattern Recognition

Pattern Classification and Scene Analysis

A fuzzy relative of the ISODATA process and its use in detecting compact, well separated clusters

J. Cybernet.

A physical interpretation of fuzzy ISODATA

IEEE Trans. Systems Man. Cybernet.

A graph theoretic analysis of pattern classification via Tamura's fuzzy relation

IEEE Trans. Systems Man. Cybernet.

Fuzzy Switching and Automata: Theory and Applications

Fuzzy Sets and Systems: Theory and Applications

Clustering analysis