Fuzzy Clustering Using Hybrid Fuzzy c-means and Fuzzy Particle Swarm Optimization
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F09%3A86077772" target="_blank" >RIV/61989100:27240/09:86077772 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Fuzzy Clustering Using Hybrid Fuzzy c-means and Fuzzy Particle Swarm Optimization
Original language description
Fuzzy clustering is an important problem which is the subject of active research in several real world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement. However FCM is sensitive to initialization and is easily trapped in local optima. Particle swarm optimization (PSO) is a stochastic global optimization tool which is used in many optimization problems. In this paper a hybrid fuzzy clusteringmethod based on FCM and fuzzy PSO (FPSO) is proposed which make use of the merits of both algorithms. Experimental results show that our proposed method is efficient and can reveal encouraging results.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
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Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2009
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
World Congress on Nature & Biologically Inspired Computing, 2009. NaBIC 2009
ISBN
978-1-4244-5053-4
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
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Publisher name
IEEE Computer Society
Place of publication
Los Alamitos, California
Event location
Indie
Event date
Dec 9, 2009
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000288686500304