DocumentCode :
547702
Title :
Fuzzy improved genetic k-means algorithm
Author :
Bozorgnia, Arezoo ; Zargar, Samaneh Hajy Mahdizadeh ; Yaghmaee, Mohammad.H
Author_Institution :
Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
fYear :
2011
fDate :
17-19 May 2011
Firstpage :
1
Lastpage :
6
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 :
Algorithm design and analysis; Biological cells; Classification algorithms; Clustering algorithms; Fuzzy sets; Genetic algorithms; Genetics; 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, Iran
Print_ISBN :
978-1-4577-0730-8
Electronic_ISBN :
978-964-463-428-4
Type :
conf
Filename :
5955591
Link To Document :
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