A possibilistic fuzzy c-means clustering algorithm

In 1997, we proposed the fuzzy-possibilistic c-means (FPCM) model and algorithm that generated both membership and typicality values when clustering unlabeled data. FPCM constrains the typicality values so that the sum over all data points of typicalities to a cluster is one. The row sum constraint...

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Veröffentlicht in:	IEEE transactions on fuzzy systems Jg. 13; H. 4; S. 517 - 530
Hauptverfasser:	Pal, N.R., Pal, K., Keller, J.M., Bezdek, J.C.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.08.2005 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	c-means models Clustering algorithms Computer science Engineering management fuzzy clustering Fuzzy logic Fuzzy systems Government hybrid clustering Knowledge based systems Phase change materials possibilistic clustering Prototypes Strontium Studies
ISSN:	1063-6706, 1941-0034
Online-Zugang:	Volltext
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