research-article

Gaussian-kernel c-means clustering algorithms

Authors:

Shou-Jen Chang-Chien,

Yessica Nataliani,

Miin-Shen YangAuthors Info & Claims

Soft Computing, Volume 25, Issue 3

Pages 1699 - 1716

https://doi.org/10.1007/s00500-020-04924-6

Published: 01 February 2021 Publication History

Abstract

Partitional clustering is the most used in cluster analysis. In partitional clustering, hard c-means (HCM) (or called k-means) and fuzzy c-means (FCM) are the most known clustering algorithms. However, these HCM and FCM algorithms work worse for data sets in a noisy environment and get inaccuracy when the data set has different shape clusters. For solving these drawbacks in HCM and FCM, Wu and Yang (Pattern Recognit 35:2267–2278, 2002) proposed the alternative c-means clustering with an exponential-type distance that extends HCM and FCM into alternative HCM (AHCM) and alternative FCM (AFCM). In this paper, we construct a more generalization of AHCM and AFCM with Gaussian-kernel c-means clustering, called GK-HCM and GK-FCM. For theoretical behaviors of GK-FCM, we analyze the bordered Hessian matrix and then give the theoretical properties of the GK-FCM algorithm. Some numerical and real data sets are used to compare the proposed GK-HCM and GK-FCM with AHCM and AFCM methods. Experimental results and comparisons actually demonstrate these good aspects of the proposed GK-HCM and GK-FCM algorithms with its effectiveness and usefulness. Finally, we apply the GK-FCM algorithm to MRI segmentation.

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Cited By

Huang JLu XYang J(2024)Sparse Membership Affinity Lasso for Fuzzy ClusteringIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2023.332768832:3(1553-1563)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1109/TFUZZ.2023.3327688
Kalti KTouil A(2023)A Robust Contextual Fuzzy C-Means Clustering Algorithm for Noisy Image SegmentationJournal of Classification10.1007/s00357-023-09443-140:3(488-512)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1007/s00357-023-09443-1

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Published In

cover image Soft Computing - A Fusion of Foundations, Methodologies and Applications

Soft Computing - A Fusion of Foundations, Methodologies and Applications Volume 25, Issue 3

Feb 2021

827 pages

ISSN:1432-7643

EISSN:1433-7479

Issue’s Table of Contents

© Springer-Verlag GmbH Germany, part of Springer Nature 2020.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 February 2021

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Cited By

Huang JLu XYang J(2024)Sparse Membership Affinity Lasso for Fuzzy ClusteringIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2023.332768832:3(1553-1563)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1109/TFUZZ.2023.3327688
Kalti KTouil A(2023)A Robust Contextual Fuzzy C-Means Clustering Algorithm for Noisy Image SegmentationJournal of Classification10.1007/s00357-023-09443-140:3(488-512)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1007/s00357-023-09443-1

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