Data Clustering with Differential Evolution Incorporating Macromutations (CROSBI ID 601703)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Martinović, Goran ; Bajer, Dražen
engleski
Data Clustering with Differential Evolution Incorporating Macromutations
Data clustering is one of the fundamental tools in data mining and requires the grouping of a dataset into a specified number of nonempty and disjoint subsets. Beside the usual partitional and hierarchical methods, evolutionary algorithms are employed for clustering as well. They are able to find good quality partitions of the dataset and successfully solve some of the shortcomings that the k-means, being one of the most popular partitional algorithms, exhibits. This paper proposes a differential evolution algorithm that includes macromutations as an additional exploration mechanism. The application probability and the intensity of the macromutations are dynamically adjusted during runtime. The proposed algorithm was compared to four variants of differential evolution and one particle swarm optimization algorithm. The experimental analysis conducted on a number of real datasets showed that the proposed algorithm is stable and manages to find high quality solutions.
Data clustering; Davies-Bouldin index; differential evolution; macromutations; representative points
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Podaci o prilogu
158-169.
2013.
objavljeno
Podaci o matičnoj publikaciji
Lecture Notes in Computer Science, Vol. 8297, Part I, Proceedings of the 4th International Conference on Swarm, Evolutionary, and Memetic Computing
Panigrahi, B.K. ; Suganthan, P.N. ; Das, S. ; Dash S.S.
Springer
978-3-319-03752-3
Podaci o skupu
Fourth International Conference on Swarm, Evolutionary, and Memetic Computing, SEMCCO 2013
predavanje
19.12.2013-21.12.2013
Chennai, Indija