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Instance: Niknam2010

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AUTHORS Niknam, T., Amiri, B.
bibtex-reference @article{Niknam2010, title = "An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis", journal = "Applied Soft Computing", volume = "10", number = "1", pages = "183 - 197", year = "2010", note = "", issn = "1568-4946", doi = "10.1016/j.asoc.2009.07.001", url = "http://www.sciencedirect.com/science/article/pii/S1568494609000854", author = "Taher Niknam and Babak Amiri", keywords = "Ant colony optimization (ACO)", keywords = "Data clustering", keywords = "Hybrid evolutionary optimization algorithm", keywords = "k-means clustering", keywords = "Fuzzy adaptive particle swarm optimization (FAPSO)", abstract = "Clustering is a popular data analysis and data mining technique. A popular technique for clustering is based on k-means such that the data is partitioned into K clusters. However, the k-means algorithm highly depends on the initial state and converges to local optimum solution. This paper presents a new hybrid evolutionary algorithm to solve nonlinear partitional clustering problem. The proposed hybrid evolutionary algorithm is the combination of FAPSO (fuzzy adaptive particle swarm optimization), ACO (ant colony optimization) and k-means algorithms, called FAPSO-ACO‰ÛÒK, which can find better cluster partition. The performance of the proposed algorithm is evaluated through several benchmark data sets. The simulation results show that the performance of the proposed algorithm is better than other algorithms such as PSO, ACO, simulated annealing (SA), combination of PSO and SA (PSO‰ÛÒSA), combination of ACO and SA (ACO‰ÛÒSA), combination of PSO and ACO (PSO‰ÛÒACO), genetic algorithm (GA), Tabu search (TS), honey bee mating optimization (HBMO) and k-means for partitional clustering problem." }
label Niknam2010
original-source-url http://www.sciencedirect.com/science/article/pii/S1568494609000854
standard-reference Niknam, T.; Amiri, B. (2010). "An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis". Applied Soft Computing 10 (1): 183Ð197. doi:10.1016/j.asoc.2009.07.001
TYPE Research

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