竞争与动态合作学习聚类分析算法

Competitive and dynamic cooperative learning algorithm

从竞争与合作学习(CCL)算法的半合作机制出发,提出了基于自适应调节合作群体内种子学习率机制的竞争与动态合作学习(CDCL)聚类分析算法.该算法在保证所有种子以较高精度收敛性于各聚类中心的同时,可有效克服同类算法普遍存在的对种子初始分布敏感、收敛速度不稳定及无法适用于异构聚类分析等问题.混合高斯聚类分析与彩色图像分割实验结果验证了CDCL的有效性,且在复杂条件下其聚类分析性能远超出CCL及其他RPCL衍生算法.

Although rival penalized competitive learning（RPCL） and its variants can select cluster numbers automatically,they have some important limitations.Based on the semi-competitive learning mechanism of competitive and cooperative learning（CCL）,this paper presents a new robust dynamic cooperative learning algorithm,or competitive and dynamic cooperative learning（CDCL）,in which the learning rate of each seed point within a cooperative team can be adaptively adjusted.CDCL not only inherits the merits of CCL,RPCL and its variants,but also overcomes most of their shortcomings.It is insensitive to the initialization of seed points and applicable to heterogeneous clusters with an attractive and accurate convergence property.Experiments on Gaussian mixture clustering and color image segmentation have shown the efficacy of CDCL.Moreover,in complex cases its performance was far superior to CCL and some other variants of RPCL.