Data mining based on adaptive artificial immune network algorithm

An adaptive artificial immune network algorithm for clustering,based on artificial Immune network model,is presented in this paper.The algorithm has the ability to achieve final network structure well-imaging the crude data feature because its parameters,such as immune suppression threshold among the antibodies,clone number of antibodies,selected and re-selected number of antibodies,the maturation magnitude of antibodies,are all self-adapted well to the entire network structure during the process of evolution.The algorithm can also relieve the dependence on prior knowledge of decision maker and enlarge the application situation and solve the relativity between the algorithm and the issue.Simulation results demonstrate the validity and the practicability of the proposed algorithm.