Clustering Based on Genetic Algorithm
FU Jingguang,XU Gang,WANG Yuguo (Institute of Software, Chinese Academy of Sciences, Beijing 100080)
A clustering method based on genetic algorithm is presented. The cluster centers are binary encoded. The sum of the Euclidean distances of the points from their respective cluster centers is adopted as the similarity metric. The optimal cluster centers are searched by selection, crossover and mutation. Experimental results demonstrate that GA-based clustering is better than K-means algorithm.