Journal
IEEE TRANSACTIONS ON FUZZY SYSTEMS
Volume 29, Issue 5, Pages 1173-1187Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2020.2969907
Keywords
Clustering algorithms; Fuzzy sets; Noise measurement; Phase change materials; Uncertainty; Linear programming; Partitioning algorithms; Cardinality; center of gravity (COG); characteristics; fuzzy clustering; type-2 fuzzy sets (T2FS)
Funding
- National Natural Science Foundation of China [11971065, 11571001, 11701338]
- Fujian Provincial Key Laboratory of Data Intensive Computing
- Fujian Provincial Big Data Research Institute of Intelligent Manufacturing
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Type-2 fuzzy sets are efficient in handling uncertainties and noisy observations. The characteristic-based type-2 fuzzy clustering algorithm proposed in this article optimizes parameter derivation formulas, enhances clustering efficiency, and effectively detects noise while assigning suitable membership degrees to data.
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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