A Novel Data-Driven Approach to Autonomous Fuzzy Clustering

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. Bas...

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Vydané v:IEEE transactions on fuzzy systems Ročník 30; číslo 6; s. 2073 - 2085
Hlavní autori: Gu, Xiaowei, Ni, Qiang, Tang, Guolin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.06.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Clustering
Clustering algorithms
Data driven
Data mining
Data models
fuzzy clustering
Kernel
Linear programming
locally optimal partition
medoids
Nickel
Optimization
Partitioning algorithms
pattern recognition
ISSN:1063-6706, 1941-0034
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2021.3074299