A Novel Data-driven Approach to Autonomous Fuzzy Clustering

Xiaowei Gu, Qiang Ni, Guolin Tang

Research output: Contribution to journalArticlepeer-review

4 Citations (SciVal)
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Abstract

In this article, a new data-driven autonomous fuzzy clustering (AFC) algorithm is proposed for static data clustering. Employing a Gaussian-type membership function, AFC first uses all the data samples as microcluster 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.

Original languageEnglish
Pages (from-to)2073-2085
Number of pages13
JournalIEEE Transactions on Fuzzy Systems
Volume30
Issue number6
Early online date20 Apr 2021
DOIs
Publication statusPublished - 01 Jun 2022
Externally publishedYes

Keywords

  • Clustering algorithms
  • Partitioning algorithms
  • Kernel
  • Linear programming
  • Data models
  • Nickel
  • Data mining
  • Data driven
  • fuzzy clustering
  • locally optimal partition
  • medoids
  • pattern recognition
  • ALGORITHM
  • SELECTION

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