Multi-Objective Differential Evolution for Automatic Clustering with Application to Micro-Array Data Analysis
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Suresh, Kaushik | - |
dc.contributor.author | Kundu, Debarati | - |
dc.contributor.author | Ghosh, Sayan | - |
dc.contributor.author | Das, Swagatam | - |
dc.contributor.author | Abraham, Ajith | - |
dc.contributor.author | Han, Sang Yong | - |
dc.date.accessioned | 2023-03-08T23:53:42Z | - |
dc.date.available | 2023-03-08T23:53:42Z | - |
dc.date.issued | 2009-05 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.issn | 1424-3210 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/65254 | - |
dc.description.abstract | This paper applies the Differential Evolution (DE) algorithm to the task of automatic fuzzy clustering in a Multi-objective Optimization (MO) framework. It compares the performances of two multi-objective variants of DE over the fuzzy clustering problem, where two conflicting fuzzy validity indices are simultaneously optimized. The resultant Pareto optimal set of solutions from each algorithm consists of a number of non-dominated solutions, from which the user can choose the most promising ones according to the problem specifications. A real-coded representation of the search variables, accommodating variable number of cluster centers, is used for DE. The performances of the multi-objective DE-variants have also been contrasted to that of two most well-known schemes of MO clustering, namely the Non Dominated Sorting Genetic Algorithm (NSGA II) and Multi-Objective Clustering with an unknown number of Clusters K (MOCK). Experimental results using six artificial and four real life datasets of varying range of complexities indicate that DE holds immense promise as a candidate algorithm for devising MO clustering schemes. | - |
dc.format.extent | 24 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | MDPI | - |
dc.title | Multi-Objective Differential Evolution for Automatic Clustering with Application to Micro-Array Data Analysis | - |
dc.type | Article | - |
dc.identifier.doi | 10.3390/s90503981 | - |
dc.identifier.bibliographicCitation | SENSORS, v.9, no.5, pp 3981 - 4004 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000266381100043 | - |
dc.identifier.scopusid | 2-s2.0-77449148945 | - |
dc.citation.endPage | 4004 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 3981 | - |
dc.citation.title | SENSORS | - |
dc.citation.volume | 9 | - |
dc.type.docType | Article | - |
dc.publisher.location | 스위스 | - |
dc.subject.keywordAuthor | differential evolution | - |
dc.subject.keywordAuthor | multi-objective optimization | - |
dc.subject.keywordAuthor | fuzzy clustering | - |
dc.subject.keywordAuthor | micro-array data clustering | - |
dc.subject.keywordPlus | OPTIMIZATION | - |
dc.subject.keywordPlus | ALGORITHM | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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