文章基本信息

标题：MST-Based Semi-Supervised Clustering Using M-Labeled Objects
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作者：Chen, Xiaoyun ; Huo, Mengmeng ; Liu, Yangyang 等
期刊名称：COMPUTING AND INFORMATICS
印刷版ISSN：1335-9150
出版年度：2012
卷号：31
期号：6+
页码：1557-1574
语种：English
出版社：COMPUTING AND INFORMATICS
摘要：Most of the existing semi-supervised clustering algorithms depend on pairwise constraints, and they usually use lots of priori knowledge to improve their accuracies. In this paper, we use another semi-supervised method called label propagation to help detect clusters. We propose two new semi-supervised algorithms named K-SSMST and M-SSMST. Both of them aim to discover clusters of diverse density and arbitrary shape. Based on Minimum Spanning Tree's algorithm variant, K-SSMST can automatically find natural clusters in a dataset by using K labeled data objects where K is the number of clusters. M-SSMST can detect new clusters with insufficient semi-supervised information. Our algorithms have been tested on various artificial and UCI datasets. The results demonstrate that the algorithm's accuracy is better than other supervised and semi-supervised approaches.
关键词：Data mining, semi-supervised learning, clustering, label propagation, MST;62H30, 91C20