计算机工程与科学2011，Vol.33Issue(10)：154-158,5.DOI:10.3969/j.issn.1007-130X.2011.10.028
一种基于MST的自适应优化相异性度量的半监督聚类方法
A Semi-Supervised Clustering Method of Adaptively Optimizing the Dissimilarity Based on MST
摘要
Abstract
This paper presents an MST-based semi-supervised clustering method of adaptively optimizing dissimilarity, when clustering an unlabeled data set which has the same or a similar distribution with a labeled sample in one hybrid attributes space. First, we can obtain "regular cluster regions" by u-sing a decision-tree method, and then adaptively optimize the dissimilarity of the hybrid attributes space based on the principia, "data points in the same clusters should have more similarity than those in other clusters". Finally, the optimized dissimilarity is applied to an MST-based clustering method. From some simulated experiments of several UCI data sets, we know that this kind of semi-supervised clustering method can often get better clustering quality. In the end, it gives a research expectation to disinter and popularize this method.关键词
相异性度量/半监督聚类/混合属性Key words
dissimilarity/ semi-supervised clustering! Hybrid attributes分类
信息技术与安全科学引用本文复制引用
陈新泉..一种基于MST的自适应优化相异性度量的半监督聚类方法[J].计算机工程与科学,2011,33(10):154-158,5.基金项目
江西省教育厅资助科研项目(GJJ10253) （GJJ10253）
