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Pregled bibliografske jedinice broj: 651072

Data Clustering with Differential Evolution Incorporating Macromutations


Martinović, Goran; Bajer, Dražen
Data Clustering with Differential Evolution Incorporating Macromutations // 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. (ur.).
Chennai, Indija: Springer International Publishing Switzerland, 2013. str. 158-169 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


Naslov
Data Clustering with Differential Evolution Incorporating Macromutations

Autori
Martinović, Goran ; Bajer, Dražen

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
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 International Publishing Switzerland, 2013, 158-169

ISBN
978-3-319-03752-3

Skup
Fourth International Conference on Swarm, Evolutionary, and Memetic Computing, SEMCCO 2013

Mjesto i datum
Chennai, Indija, 19-21.12.2013

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Data clustering; Davies-Bouldin index; differential evolution; macromutations; representative points

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo



POVEZANOST RADA


Projekt / tema
165-0361621-2000 - Distribuirano računalno upravljanje u transportu i industrijskim pogonima (Željko Hocenski, )
165-0362980-2002 - Postupci raspoređivanja u samoodrživim raspodijeljenim računalnim sustavima (Goran Martinović, )

Ustanove
Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek