MST-Based Semi-Supervised Clustering Using M-Labeled Objects

Authors

  • Xiaoyun Chen School of Informationh Science and Engineering, Lanzhou University
  • Mengmeng Huo School of Informationh Science and Engineering, Lanzhou University
  • Yangyang Liu School of Informationh Science and Engineering, Lanzhou University

Keywords:

Data mining, semi-supervised learning, clustering, label propagation, MST

Abstract

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.

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Published

2013-01-30

How to Cite

Chen, X., Huo, M., & Liu, Y. (2013). MST-Based Semi-Supervised Clustering Using M-Labeled Objects. Computing and Informatics, 31(6+), 1557–1574. Retrieved from http://147.213.75.17/ojs/index.php/cai/article/view/1331