Title :
A Novel Clustering Algorithm with Ant Colony Optimization
Author_Institution :
Dept. of Comput. Sci., Guangdong Polytech. Normal Univ., Guangzhou
Abstract :
Clustering analysis is an important area of data mining. A kind of new clustering algorithm with ant colony optimization based on cluster center initialization is proposed in this paper. The new algorithm gives initialized cluster centers by different methods, then solves clustering problems by iterated method. Three methods of cluster center initialization are used in clustering algorithm with ant colony optimization - Sacc.Three datasets - butterfly data, iris and wine are chosen for the compare of three algorithms. The results of several times experiments show that the new algorithm is less in running time, is better in clustering effect and more stable than Sacc. Experimental results validate new algorithm´s efficiency.
Keywords :
data mining; iterative methods; optimisation; pattern clustering; ant colony optimization; cluster center initialization; clustering algorithm; clustering analysis; data mining; iterated method; Ant colony optimization; Application software; Clustering algorithms; Computational intelligence; Computer industry; Computer science; Conferences; Mining industry; Partitioning algorithms; Pattern analysis;
Conference_Titel :
Computational Intelligence and Industrial Application, 2008. PACIIA '08. Pacific-Asia Workshop on
Conference_Location :
Wuhan
Print_ISBN :
978-0-7695-3490-9
DOI :
10.1109/PACIIA.2008.75