【摘要】 在分析K means聚类算法的优越性和存在不足的基础上 ,提出了一种新的聚类算法———基于免疫规划的K means聚类算法 .理论分析和仿真结果表明 ,该算法不仅有效地克服了传统的K means聚类算法易陷入局部极小值的缺点 ,而且明显地避免了对初始化选值敏感性的问题 ,同时也有较快的收敛速度 .更多还原

【Abstract】 This paper proposes a novel K-means clustering based on the immune programming algorithm after analyzing the advantages and disadvantages of the classical K-means clustering algorithm. The theory analysis and experimental results show that the algorithm not only avoids the local optima and is robust to initialization, but also increases the convergence speed.更多还原