Typical Characteristic-Based Type-2 Fuzzy C-Means Algorithm

Type-2 fuzzy sets provide an efficient vehicle for handling uncertainties of real-world problems, including noisy observations. Bringing type-2 fuzzy sets to clustering algorithms offers more flexibility to handle uncertainties associated with membership concepts caused by a noisy environment. Howev...

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Veröffentlicht in:IEEE transactions on fuzzy systems Jg. 29; H. 5; S. 1173 - 1187
Hauptverfasser: Yang, Xiyang, Yu, Fusheng, Pedrycz, Witold
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.05.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Cardinality
center of gravity (COG)
characteristics
Clustering
Clustering algorithms
fuzzy clustering
Fuzzy sets
Linear programming
Noise measurement
Partitioning algorithms
Phase change materials
type-2 fuzzy sets (T2FS)
Uncertainty
ISSN:1063-6706, 1941-0034
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Zusammenfassung:Type-2 fuzzy sets provide an efficient vehicle for handling uncertainties of real-world problems, including noisy observations. Bringing type-2 fuzzy sets to clustering algorithms offers more flexibility to handle uncertainties associated with membership concepts caused by a noisy environment. However, the existing type-2 fuzzy clustering algorithms suffer from a time-consuming type-reduction process, which not only hampers the clustering performance but also increases the burden of understanding the clustering results. In order to alleviate the problem, this article introduces a set of typical characteristics of type-2 fuzzy sets and establishes a characteristic-based type-2 fuzzy clustering algorithm. Being different from the objective function used in the fuzzy C-means (FCM) algorithm that produces cluster centers and type-1 memberships, the objective function in the proposed algorithm contains additional characteristics of type-2 membership grades, namely, centers of gravity and cardinalities of the secondary fuzzy sets. The derived iterative formulas used for these parameters are much more efficient than the interval type-2 FCM algorithm. The experiments carried out in this study show that the proposed typical characteristic-based type-2 FCM algorithm has an ability of detecting noise as well as assigning suitable membership degrees to the individual data.
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ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2020.2969907