一种基于D^2权重的核k-means聚类算法

The D^2 Weighting Kernel K-Means Clustering Algorithm

核k-means算法是标准k-means算法的扩展,提高了k-means聚类中对非线性不可分数据的聚类效果.传统核k-means算法的初始中心是随机选取的,导致出现聚类时间较慢、聚类性能低等问题.文中提出了一种基于D2权重的核k-means算法,它根据点对簇内距离的贡献,选取对其贡献最大的点为簇中心,然后在核空间内进行相应的聚类.在UCI数据集上进行实验,实验结果表明,新算法相对于传统的核k-means算法,可以有效地缩短聚类时间,并提高聚类的质量,新算法性能优于传统的核K-means算法.

Kernel k-means is an extension of the standard k-means that improves the quality of nonlinearly separable clusters.In order to overcome the cluster initialization problem such as long computation time and low efficiency with this method,In this paper we propose the D2 weight kernel k-means algorithm.Our method adds one cluster depending on the contribution to the sun of the distance that any point to the closest center that we have already chosen,and we choose the point as the cluster that has the most contribution.In order to verify the effectiveness of the proposed algorithm,we have done a large number of experiments on the UCI dataset.The experiment result shows that the proposed algorithm improves both the accuracy and the speed of kernel k-means.The new algorithm does better than the traditional k-means.