Adaptive fuzzy clustering based on Genetic algorithm

Author

Lianjiang, Zhu ; Shouning, Qu ; Tao, Du

Author_Institution

Coll. of Inf. Sci. & Eng., Univ. of Jinan, Jinan, China

Volume

5

fYear

2010

fDate

27-29 March 2010

Firstpage

79

Lastpage

82

Abstract

Traditional Fuzzy c-means (FCM) algorithm is commonly used in unsupervised learning. However, there are some limitations. Cluster number should be determined and the cluster center should be initialized before classification. A new algorithm is proposed in the paper. The best cluster number is obtained by analyzing cluster validity function and the cluster center is initialized by HCM. The data set is classified with Fuzzy c-means algorithm based on Genetic algorithm. The experimental results indicate the effectiveness and adaptability of the new algorithm.

Keywords

fuzzy set theory; genetic algorithms; pattern clustering; unsupervised learning; adaptive fuzzy clustering; cluster center; cluster validity function; fuzzy c-means algorithm; genetic algorithm; unsupervised learning; Algorithm design and analysis; Classification algorithms; Clustering algorithms; Educational institutions; Fuzzy sets; Genetic algorithms; Genetic engineering; Information science; Minimization methods; Unsupervised learning; Cluster Analysis; Cluster validity; Fuzzy C-means; Genetic algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Computer Control (ICACC), 2010 2nd International Conference on

Conference_Location

Shenyang

Print_ISBN

978-1-4244-5845-5

Type

conf

DOI

10.1109/ICACC.2010.5487289

Filename

5487289