A Novel Data-driven Approach to Autonomous Fuzzy Clustering

Gu, X. and Ni, Q. and Tang, G. (2022) A Novel Data-driven Approach to Autonomous Fuzzy Clustering. IEEE Transactions on Fuzzy Systems, 30 (6). pp. 2073-2085. ISSN 1063-6706

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Abstract

In this paper, a new data-driven autonomous fuzzy clustering (AFC) algorithm is proposed for static data clustering. Employing a Gaussian-type membership function, AFC firstly uses all the data samples as micro-cluster medoids to assign memberships to each other and obtains the membership matrix. Based on this, AFC chooses these data samples that represent local models of data distribution as cluster medoids for initial partition. It then continues to optimize the cluster medoids iteratively to obtain a locally optimal partition as the algorithm output. Moreover, an online extension is introduced to AFC enabling the algorithm to cluster streaming data chunk-by-chunk in a one pass manner. Numerical examples based on a variety of benchmark problems demonstrate the efficacy of the AFC algorithm in both offline and online application scenarios, proving the effectiveness and validity of the proposed concept and general principles.

Item Type:

Journal Article

Journal or Publication Title:

IEEE Transactions on Fuzzy Systems

Additional Information:

©2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Uncontrolled Keywords:

/dk/atira/pure/subjectarea/asjc/1700/1702

Subjects:

?? clustering algorithmsdata miningdata modelsdata-drivenfuzzy clusteringkernellinear programminglocally optimal partitionmedoidsnickelpartitioning algorithmspattern recognitionbenchmarkingfuzzy clusteringiterative methodsmembership functionsbench-mark probl ??

Departments:

Faculty of Science and Technology > School of Computing & Communications

ID Code:

154950

Deposited By:

ep_importer_pure

Deposited On:

17 May 2021 12:45

Refereed?:

Yes

Published?:

Published

Last Modified:

07 Sep 2024 00:28

URI:

https://eprints.lancs.ac.uk/id/eprint/154950