Genetic algorithm for clustering, finding the number of clusters

• Autorzy: Śmigielski P.
• Języki publikacji: EN

Abstrakty

EN

In this paper a genetic algorithm for clustering is proposed. The algorithm is based on the variable length chromosomes and the notion of local points density in the clustered set. Its role is to identify the number of clusters in the clustered set and to partition this set into particular clusters. The tests were conducted for two different sets of two dimensional data. The algorithm performed well in both cases. The tests presented the ability of the algorithm to partition the subsets combined with the thin dense area into separate clusters.

Słowa kluczowe

EN

genetic algorithm; clustering; variable length chromosome; cube

• Wydawca: Wydawnictwo Uniwersytetu Jagiellońskiego
• Czasopismo: Schedae Informaticae
• Rocznik: 2011
• Tom: Vol. 20
• Strony: 101--113
• Opis fizyczny: Bibliogr. 8 poz., rys.

Twórcy

autor

Śmigielski P.

• Institute of Computer Science, Jagiellonian University, Łojasiewicza 6, 30-348 Kraków, Poland,

Bibliografia

• [1] Barszcz T., Bielecki A., W´ojcik M.; ART-type artificial neural networks applications for classification of operational states in wind turbines, Lecture Notes in Artificial Intelligence, 6114, 2010, pp. 11–18.
• [2] Barszcz T., Bielecka M., Bielecki A., W´ojcik M.; Wind turbines states classification by a fuzzy-ART neural network with a stereographic projection as a signal normalization, Lecture Notes in Computer Science, 6594, 2011, pp. 225–234.
• [3] Bielecki A., Bielecka M., Chmielowiec A.; Input signals normalization in Kohonen neural networks, Lecture Notes in Artificial Intelligence, 5097, 2008, pp. 3–10.
• [4] Cavicchio D.J.; Adaptative search using simulated evolution, PhD thesis, University of Michigan, 1970.
• [5] Goldberg D.E.; Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Boston 1989.
• [6] Goswami G., Liu J.S., Wong W.H.; Evolutionary Monte Carlo methods for clustering, Journal of Computational and Graphical Statistics, 16, 2007, pp. 855–876.
• [7] Hruschka E.R., Ebecken N.F.F.; A genetic algorithm for cluster analysis, Intelligent Data Analysis, 7, 2003, pp. 15–25.
• [8] Lorena L.A.N., Furtado J.C.; Constructive genetic algorithm for clustering problems, Evolutionary Computation, 9, 2001, pp. 309–328.