一种改进的半监督K-Means聚类算法

半监督聚类利用部分标签的数据辅助未标签的数据进行学习,从而提高聚类的性能。针对基于K-means的聚类算法发现非球状簇能力差的问题,本文提出新的处理思想,即把已标签数据对未标签数据的引力影响加入到类别分配决策中,给出了类与点的引力影响度定义,设计了带引力参数的半监督K-means聚类算法。实验表明,该算法在处理非球状簇分布的聚类时比现有的半监督K-means方法效果更好。

An Improved Semi-Supervised K-Means Clustering Algorithm

Semi-supervised clustering employs a small amount of labeled data to aid unsupervised learning.For the poor ability of the clustering algorithm based on the K-means for non-spherical clusters problems,this paper presents a new idea that considers the influence of the labeled data-points on the unlabeled data-points in allocating category,puts forward a definition of gravitational influence degree between category and data-point,and designs a semi-supervised K-means clustering algorithm with a gravitational parameter.The experiments show that the new algorithm has better effect than the traditional semi-supervised K-means clustering method in dealing with the distribution of non-spherical cluster clustering.