Data Clustring Using A New CGA(Chaotic-Generic Algorithm) Approach

摘要: Clustering is the process of dividing a set input data into number subgroups. The members each subgroup are similar to other but different from genetic algorithm has enjoyed many applications in clustering data. One these images. problem with earlier methods used images was selecting initial clusters. In this article it been tried develop populations (i.e., cluster centers) using clonal selection artificial immune system, and obtain final clusters main image among large through use K-means K- nearest neighbor algorithms. Moreover, chaotic model also create diversity both original population produced repetition generations. algorithms paper have executed on satellite images; implementation results showed that works well.