Design of K-Means Clustering Algorithm Based on Distance Concentration

Using the immune recognizing principle, the data object to cluster was denoted as the antigens set, and the clustering center was the antibodies set. The clustering was the process to obtain the best antibodies to catch the antigens by producing the antibodies and recognizing the antigens unceasingly. The distance concentration and the affinity, between antibody and antigen, and between antibody and antibody, were defined about the K-means clustering; the antibody reproduction function was proposed. The antibody cloning algorithm was presented. The experimental results show that the algorithm not only avoids the local optima and is robust to initialization, but also increases the convergence speed.