DocumentCode :
3649412
Title :
Genetic algorithm for clustering accelerated by the CUDA platform
Author :
Pavel Krömer;Jan Platoš;Václav Snášel
Author_Institution :
Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu 12, Poruba, Czech Republic
fYear :
2012
Firstpage :
1005
Lastpage :
1010
Abstract :
Unsupervised clustering of large data sets is a complicated NP-hard task. Due to its complexity, various metaheuristic machine learning algorithms have been used to automate or aid the clustering process. Genetic and evolutionary algorithms have been deployed to find clusters in data sets with success. However, also evolutionary clustering suffers from the high computational demands when it comes to fitness function evaluation. The GPU computing is a recent programming and development paradigm introducing high performance parallel computing to general audience. This work presents an initial design and implementation of a genetic algorithm for density based clustering on the GPU using the nVidia CUDA platform.
Keywords :
"Graphics processing units","Indexes","Kernel","Genetic algorithms","Biological cells","Clustering algorithms","Encoding"
Publisher :
ieee
Conference_Titel :
Systems, Man, and Cybernetics (SMC), 2012 IEEE International Conference on
Print_ISBN :
978-1-4673-1713-9
Type :
conf
DOI :
10.1109/ICSMC.2012.6377860
Filename :
6377860
Link To Document :
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