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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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.

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Journal Article
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IEEE Transactions on Fuzzy Systems
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Uncontrolled Keywords:
?? clustering algorithmsdata miningdata modelsdata-drivenfuzzy clusteringkernellinear programminglocally optimal partitionmedoidsnickelpartitioning algorithmspattern recognitionbenchmarkingfuzzy clusteringiterative methodsmembership functionsbench-mark probl ??
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17 May 2021 12:45
Last Modified:
09 Apr 2024 00:12