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An enhanced progressive fuzzy clustering approach to pattern recognition

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

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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)
Uncontrolled Keywords: Pattern recognition, cluster set theory, fuzzy algorithms
Subjects: FOR Classification > 0802 Computation Theory and Mathematics
Faculty/School/Research Centre/Department > School of Engineering and Science
Depositing User: VU Library
Date Deposited: 19 May 2010 04:58
Last Modified: 23 May 2013 16:42
URI: http://vuir.vu.edu.au/id/eprint/15324
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