Title :
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
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;
Conference_Titel :
Advanced Computer Control (ICACC), 2010 2nd International Conference on
Conference_Location :
Shenyang
Print_ISBN :
978-1-4244-5845-5
DOI :
10.1109/ICACC.2010.5487289
