A possibilistic fuzzy Gath-Geva clustering algorithm using the exponential distance

[Display omitted] •Propose PFGG clustering algorithm.•PFGG uses the exponential distance in contrast to PFCM.•PFGG clusters the dataset containing noisy data accurately.•The clustering accuracies, cluster centers and convergence results of PFGG are significantly better than FCM, GG, PFCM. As a famou...

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Veröffentlicht in:Expert systems with applications Jg. 184; S. 115550
Hauptverfasser: Wu, Xiaohong, Zhou, Haoxiang, Wu, Bin, Zhang, Tingfei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Elsevier Ltd 01.12.2021
Elsevier BV
Schlagworte:
Algorithms
Clustering
Covariance matrix
Euclidean geometry
GG clustering
Hyperellipsoidal data
Noise data
Noise sensitivity
PFCM clustering
PFGG clustering
PFCM clustering
PFGG clustering
Hyperellipsoidal data
GG clustering
Noise data
ISSN:0957-4174, 1873-6793
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Zusammenfassung:[Display omitted] •Propose PFGG clustering algorithm.•PFGG uses the exponential distance in contrast to PFCM.•PFGG clusters the dataset containing noisy data accurately.•The clustering accuracies, cluster centers and convergence results of PFGG are significantly better than FCM, GG, PFCM. As a famous clustering algorithm, Gath-Geva (GG) clustering has been widely applied in many fields. Unlike fuzzy c-means (FCM) clustering, GG clustering uses the exponential distance with the fuzzy covariance matrix instead of Euclidean distance to cluster hyperellipsoidal data accurately. Due to the existence of probabilistic constraint, GG clustering is sensitive to noise data. For possibilistic fuzzy c-means (PFCM), the type of clustering data limits its application. To solve the noise sensitivity problem of GG clustering and extend PFCM for clustering hyperellipsoidal data, a possibilistic fuzzy Gath-Geva (PFGG) clustering algorithm is proposed. The PFGG clustering is derived from GG clustering in combination with PFCM clustering. FCM, GG, PFCM and PFGG are run on the several datasets to compare their clustering results. The experimental results show that the performance of PFGG is significantly better than the other clustering algorithms.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2021.115550