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A Novel Data-driven Approach to Autonomous Fuzzy Clustering

Gu, Xiaowei, Ni, Qiang, Tang, Guolin (2021) A Novel Data-driven Approach to Autonomous Fuzzy Clustering. IEEE Transactions on Fuzzy Systems, . p. 1. ISSN 1063-6706. E-ISSN 1941-0034. (doi:10.1109/TFUZZ.2021.3074299) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:90395)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
https://doi.org/10.1109/TFUZZ.2021.3074299

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: Article
DOI/Identification number: 10.1109/TFUZZ.2021.3074299
Uncontrolled keywords: Clustering algorithms; Partitioning algorithms; Kernel; Linear programming; Data models; Nickel; Data mining; data-driven; fuzzy clustering; locally optimal partition; medoids; pattern recognition
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Amy Boaler
Date Deposited: 28 Sep 2021 09:03 UTC
Last Modified: 29 Sep 2021 11:35 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90395 (The current URI for this page, for reference purposes)
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