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An improved clustering method based on artificial immune is proposed. To obtain the better initial solution, the initial antibody network is introduced by self organizing map (SOM) method. In the process of the clustering iteration, a series of optimization and evolution strategies are designed, such as clustering satisfaction, the threshold design of scale compression, the learning rate, the clustering monitoring points and the clustering evaluations indexes. These strategies can make the clustering thresholds be quantified and reduce the operator’s subjective factors. Thus, the local optimal and the global optimal clustering simultaneously are proposed by the synthesized function of these strategies. Finally, the experiment and the comparisons demonstrate the proposed method effectiveness.
An improved clustering method based on artificial immune is proposed. The initial antibody network is introduced by self organizing map (SOM) method. In the process of the clustering iteration, a series of optimization and evolution strategies are designed , such as clustering satisfaction metrics, the threshold design of scale compression, the learning rate, the clustering monitoring points and quantifying evaluations indexes. Thus, the local optimal and the global optimal clustering simultaneously are proposed by the synthesized function of these strategies. Finally, the experiment and the comparisons demonstrate the proposed method effectiveness.