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

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

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

image clustering , genetic algorithm , chaotic model , Satellite image , Chaos-Genetic Algorithm , Fitness