General Type-2 Fuzzy C-Means Algorithm for Uncertain Fuzzy Clustering

Pattern recognition in real-world data is subject to various sources of uncertainty that should be appropriately managed. The focus of this paper is the management of uncertainty associated with parameters of fuzzy clustering algorithms. Type-2 fuzzy sets (T2 FSs) have received increased research in...

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Vydáno v:IEEE transactions on fuzzy systems Ročník 20; číslo 5; s. 883 - 897
Hlavní autoři: Linda, O., Manic, M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.10.2012
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Clustering algorithms
Construction
Frequency selective surfaces
Fuzzy
Fuzzy C-means
Fuzzy logic
Fuzzy set theory
Fuzzy sets
general type-2 fuzzy sets
Linguistics
Mathematical models
Partitioning algorithms
pattern recognition
Pragmatics
Studies
Uncertainty
α-planes representation
ISSN:1063-6706, 1941-0034
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Shrnutí:Pattern recognition in real-world data is subject to various sources of uncertainty that should be appropriately managed. The focus of this paper is the management of uncertainty associated with parameters of fuzzy clustering algorithms. Type-2 fuzzy sets (T2 FSs) have received increased research interest over the past decade, primarily due to their potential to model various uncertainties. However, because of the computational intensity of the processing of general T2 fuzzy sets (GT2 FSs), only their constrained version, i.e., the interval T2 (IT2) FSs, were typically used. Fortunately, the recently introduced concepts of α-planes and zSlices allow for efficient representation and computation with GT2 FSs. Following this recent development, this paper presents a novel approach for uncertain fuzzy clustering using the general type-2 fuzzy C-means (GT2 FCM) algorithm. The proposed method builds on top of the previously published IT2 FCM algorithm, which is extended via the α- planes representation theorem. The fuzzifier parameter of the FCM algorithm can be expressed using linguistic terms such as "small" or "high," which are modeled as T1 FSs. This linguistic fuzzifier value is then used to construct the GT2 FCM cluster membership functions. The linguistic uncertainty is transformed into uncertain fuzzy positions of the extracted clusters. The GT2 FCM algorithm was found to balance the performance of T1 FCM algorithms in various uncertain pattern recognition tasks and to provide increased robustness in situations where noisy or insufficient training data are present.
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ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2012.2187453