Application of adaptive artificial immune algorithm to data mining
自适应人工免疫算法在数据挖掘中的应用

In the artificial immune algorithm for clustering analysis,its suppression and stimulate thresholds determine cluster precision and network population scale.Theses thresholds adopt fixed value that is decided according to the problem characteristic itself and user's experience.However,this modus operandi results in narrow application situation and is dependent heavily on problem characteristic itself.Therefore,an adaptive artificial immune algorithm for clustering was proposed.This algorithm could achieve final network structure well matching the crude data feature and relieve the dependence on problem characteristic itself,because its thresholds were obtained from the dynamic immune network structure and were adapted well to the entire network structure during the process of evolution.Experimental results demonstrate its effectiveness.