基于加权处罚的K-均值优化算法

在各种聚类算法中,基于目标函数的K-均值聚类算法应用最为广泛,然而,K-均值算法对初始聚类中心特别敏感,聚类结果易收敛于局部最优。为此,提出基于加权处罚的K-均值优化算法。每次迭代过程中,根据簇的平均误差的大小为簇分配权值,构造加权准则函数,把样本分给加权距离最小的簇中。限制簇集中出现平均误差较大的簇,提高聚类准确率。实验结果表明,该算法与K-均值算法、优化初始聚类中心的K-均值算法相比,在含有噪音的数据集中,表现出更好的抗噪性能,聚类效果更好。

An Optimal K-means Algorithm Based on Weighted Penalty

In a variety of clustering algorithms, K-means clustering algorithm which is based on the objective function has the most widely used, However, K-means is sensible to the initial seeds, poor local optima can be easily ob-tained. To tackle the initialization problem of K-means, an optimal K-Means algorithm based on weighted penalty is proposed. In each iteration process, the weights are assigned for the clusters relative to their average variance;a weighted version of K-means objective is constructed;the samples are taken to the clusters of minimum weighted dis-tance. The emergence of large average variance clusters is limited and the clustering accuracy is improved. The effec-tiveness of the approach is verified in experiments and the immune property with noises is got in its clustering,as it is compared favorably with both K-means and other methods from the literature that consider the K-means initialization problem.