DocumentCode :
548028
Title :
Fuzzy improved genetic k-means algorithm
Author :
Bozorgnia, A. ; Zargar, Samaneh Hajy Mahdizadeh ; Yaghmaee, Mohammad Hossein
Author_Institution :
Dept. of Comput. Eng., Islamic Azad Univ., Mashhad, Iran
fYear :
2011
fDate :
17-19 May 2011
Firstpage :
1
Lastpage :
1
Abstract :
Clustering is a significant technique in data mining. Many methods for increasing the ability of clustering large data have been presented, one appropriate technique is the k-means method which has been combined with Artificial intelligence methods like genetic algorithm and has created an optimal performance. In primary clustering algorithms the clustering result depends on the initial centers of the clusters. In the presentation of an optimized clustering technique, apart from considering the current issues of clustering, it has tried to find the optimized number of clusters in the clustering procedure. The proposed technique increases the performance and integration of the k-means genetic algorithm with the use of fuzzy methods.
Keywords :
artificial intelligence; data mining; fuzzy set theory; genetic algorithms; pattern clustering; artificial intelligence; data clustering; data mining; fuzzy improved genetic k-means algorithm; fitness factor; fuzzy method; genetic algorithm; k-means algorithm;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical Engineering (ICEE), 2011 19th Iranian Conference on
Conference_Location :
Tehran
Print_ISBN :
978-1-4577-0730-8
Type :
conf
Filename :
5955918
Link To Document :
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