【摘要】 针对谱聚类算法中K-means处理无标识软件度量元数据易陷入局部最优的问题,提出一种新的混沌免疫聚类算法.该方法在免疫克隆选择算法的框架下,设计抗体亲和度计算方法用于免疫克隆聚类中心的评价,并给出分层混沌变异算子,进一步提高了无标识软件度量元数据的预测性能.仿真实验验证了算法的有效性.更多还原

【Abstract】 Aiming at the problem that non marking software metric metadata was easy to fall into the local optimum of the K-means process in the spectral clustering algorithm,we proposed a new algorithm of chaotic immune clustering algorithm.Under the framework of the immune clonal selection algorithm,the method of antibody affinity calculation was designed for the evaluation of immune clonal clustering center,and the hierarchical chaotic mutation operator was given,which further improved prediction performance of non marking software metric metadata.Simulation results show that the algorithm is effective.更多还原