An enhanced progressive fuzzy clustering approach to pattern recognition

Export

Im, Paul Poh Teng (1997) An enhanced progressive fuzzy clustering approach to pattern recognition. PhD thesis, Victoria University of Technology.

Abstract

This thesis applies an enhanced progressive clustering approach, involving fuzzy clustering algorithms and fuzzy neural networks, to solve some practical problems of pattern recognition. A new fuzzy clustering framework, referred to as Cluster Prototype Centring by Membership (CPCM), has been developed. A Possibilistic Fuzzy c-Means algorithm(PFCM), which is also new, has been formulated to investigate properties of fuzzy clustering. PFCM extends the useability of the Fuzzy c-Means (FCM) algorithm by generalisation of the membership function.

Item type Thesis (PhD thesis)
URI https://vuir.vu.edu.au/id/eprint/15324
Subjects Historical > FOR Classification > 0802 Computation Theory and Mathematics
Historical > Faculty/School/Research Centre/Department > School of Engineering and Science
Keywords Pattern recognition, cluster set theory, fuzzy algorithms
Download/View statistics View download statistics for this item

Search Google Scholar

Repository staff login

CORE (COnnecting REpositories)