An enhanced progressive fuzzy clustering approach to pattern recognition

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.

Name dc:type.qualificationname

phd

Level dc:type.qualificationlevel

doctoral

Grantor dc:publisher.institution

Victoria University of Technology

Year dc:date.issued

1997

Author dc:creator

• Im, Paul Poh Teng

• 0802 Computation Theory and Mathematics
• School of Engineering and Science

Language dc:language

en