Accelerating Fuzzy-C Means Using an Estimated Subsample Size

Many algorithms designed to accelerate the fuzzy c-means (FCM) clustering algorithm randomly sample the data. Typically, no statistical method is used to estimate the subsample size, despite the impact subsample sizes have on speed and quality. This paper introduces two new accelerated algorithms, i...

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Veröffentlicht in:	IEEE transactions on fuzzy systems Jg. 22; H. 5; S. 1229 - 1244
Hauptverfasser:	Parker, Jonathon K., Hall, Lawrence O.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.10.2014 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accelerated Acceleration Algorithm design and analysis Algorithms Clustering algorithms Estimates extensible fast fuzzy c-means (EFFCM) Fuzzy fuzzy c-means (FCM) fuzzy clustering Fuzzy logic Fuzzy set theory geometric progressive fuzzy c-means (GOFCM) minimum sample estimate random fuzzy c-means (MSERFCM) multinomial proportion online fuzzy c-means (OFCM) Optimization Partitioning algorithms Partitions progressive sampling random sampling plus extension fuzzy c-means (rseFCM) Runtime Samples sampling scalable single-pass fuzzy c-means (SPFCM) Statistical analysis Statistical methods stopping criterion rsefcm scalable progressive sampling sampling fuzzy c-means multinomial proportion accelerated effcm fcm mserfcm ofcm fuzzy clustering gofcm stopping criterion spfcm
ISSN:	1063-6706, 1941-0034
Online-Zugang:	Volltext
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