摘要
针对传统K-均值算法容易受到野点和噪声点的影响,缺乏鲁棒性的问题,提出了一种基于协同熵的K-均值算法。该方法利用协同熵作为一种局部的相似度度量手段,并依赖最大协同熵准则进行最优聚类中心的求解。采用迭代重加权的优化算法可以用来快速实现最优聚类中心的求解。对于残差较大的野点和噪声,它们在聚类中心更新的过程中将被赋予较小的权重。实验结果表明,基于协同熵的K-均值算法具有较好的鲁棒性,并获得较好的聚类效果。
Considering the fact that conventional K-means algorithm is susceptible to the outliers and noise points,and lacking in robustness,a new K-means algorithm based on co-entropy is proposed. The proposed algorithm employs co-entropy as a means of local similarity measurement,and follows the co-entropy maximization principle to solve the optimal cluster centers. An iteratively reweighted optimization technique is employed to quickly find the optimal cluster centers. For outliers and noisy data points with larger residuals,they will be assigned smaller weights in updating the cluster centers. Experimental results demonstrate that the proposed co-entropy based K-means algorithm is robust,winning a better clustering effect.
出处
《电光与控制》
北大核心
2015年第7期66-69,共4页
Electronics Optics & Control
