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An enhanced progressive fuzzy clustering approach to pattern recognition

Paul Poh Teng Im-1997-01-01-Victoria University Research Repository (Victoria University)
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TL;DRAbstract

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.

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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.

Keywords

Cluster analysisCentringFuzzy clusteringFuzzy logicData miningArtificial intelligenceFLAME clusteringPattern recognition (psychology)

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