Abstract:
A multi-objective immune clonal clustering method based on Parzen density estimation is proposed after analyzing disadvantages of k-means and immune evolutionary clustering. Aiming at the problem that clonal scale is hard to be determined in multi-objective immune clonal algorithm, a method based on density clustering is developed by using kernel density estimation. And the clonal scale of antibody is determined by density and generation. Chaotic mutation is introduced into this method to increase antibody diversity. Finally, TOPSIS (technique for order preference by similarity to an ideal solution) is used to choose antibodies. The simulation experiments on artificial and UCI (universal chess interface) data sets show that this algorithm has a higher speed and better clustering result.