Fuzzy Clustering to Identify Clusters at Different Levels of Fuzziness: An Evolutionary Multiobjective Optimization Approach

Fuzzy clustering methods identify naturally occurring clusters in a dataset, where the extent to which different clusters are overlapped can differ. Most methods have a parameter to fix the level of fuzziness. However, the appropriate level of fuzziness depends on the application at hand. This paper...

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Vydané v:IEEE transactions on cybernetics Ročník 51; číslo 5; s. 2601 - 2611
Hlavní autori: Gupta, Avisek, Datta, Shounak, Das, Swagatam
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.05.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Clustering
Clustering algorithms
Clustering methods
Cybernetics
Datasets
Electronic countermeasures
Entropy
Evolutionary algorithms
fuzzy <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">c -means (FCM)
fuzzy clustering
Genetic algorithms
Linear programming
multiobjective clustering
Multiple objective analysis
Optimization
Pareto optimization
Sorting algorithms
ISSN:2168-2267, 2168-2275, 2168-2275
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Shrnutí:Fuzzy clustering methods identify naturally occurring clusters in a dataset, where the extent to which different clusters are overlapped can differ. Most methods have a parameter to fix the level of fuzziness. However, the appropriate level of fuzziness depends on the application at hand. This paper presents an entropy <inline-formula> <tex-math notation="LaTeX">{c} </tex-math></inline-formula>-means (ECM), a method of fuzzy clustering that simultaneously optimizes two contradictory objective functions, resulting in the creation of fuzzy clusters with different levels of fuzziness. This allows ECM to identify clusters with different degrees of overlap. ECM optimizes the two objective functions using two multiobjective optimization methods, nondominated sorting genetic algorithm II (NSGA-II) and multiobjective evolutionary algorithm based on decomposition (MOEA/D). We also propose a method to select a suitable tradeoff clustering from the Pareto front. Experiments on challenging synthetic datasets as well as real-world datasets show that ECM leads to better cluster detection compared to the conventional fuzzy clustering methods as well as previously used multiobjective methods for fuzzy clustering.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2019.2907002