Multiobjective fuzzy clustering approach based on tissue-like membrane systems
| Autor: | Mario J. Prez-Jimnez, Jun Wang, Agustn Riscos-Nez, Hong Peng, Peng Shi |
|---|---|
| Přispěvatelé: | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad de Sevilla. TIC193: Computación Natural |
| Rok vydání: | 2017 |
| Předmět: |
Information Systems and Management
Fuzzy clustering Computer science Correlation clustering Membrane Computing 02 engineering and technology computer.software_genre Multiobjective clustering problem Management Information Systems Biclustering Artificial Intelligence CURE data clustering algorithm Consensus clustering 0202 electrical engineering electronic engineering information engineering Cluster analysis 05 social sciences 050301 education Data set ComputingMethodologies_PATTERNRECOGNITION Tissue-like membrane systems Canopy clustering algorithm FLAME clustering 020201 artificial intelligence & image processing Data mining 0503 education computer Software |
| Zdroj: | idUS. Depósito de Investigación de la Universidad de Sevilla instname |
| ISSN: | 0950-7051 6147-2328 |
| DOI: | 10.1016/j.knosys.2017.03.024 |
| Popis: | Fuzzy clustering problem is usually posed as an optimization problem. However, the existing researchhas shown that clustering technique that optimizes a single cluster validity index may not provide satisfactory results on different kinds of data sets. This paper proposes a multiobjective clustering frameworkfor fuzzy clustering, in which a tissue-like membrane system with a special cell structure is designed tointegrate a non-dominated sorting technique and a modified differential evolution mechanism. Based onthe multiobjective clustering framework, a fuzzy clustering approach is realized to optimize three cluster validity indices that can capture different characteristics. The proposed approach is evaluated on sixartificial and ten real-life data sets and is compared with several multiobjective and singleobjective techniques. The comparison results demonstrate the effectiveness and advantage of the proposed approachon clustering the data sets with different characteristics. National Natural Science Foundation of China No 61472328 Chunhui Project Foundation of the Education Department of China No. Z2016148 Chunhui Project Foundation of the Education Department of China No. Z2016143 Research Foundation of the Education Department of Sichuan province No. 17TD0034 |
| Databáze: | OpenAIRE |
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