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

In: Computing and Informatics, vol. 31, no. 6+
X. Chen - M. Huo - Y. Liu
Detaily:
Rok, strany: 2012, 1557 - 1574
Kľúčové slová:
Data mining, semi-supervised learning, clustering, label propagation, MST
O článku:
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.
Ako citovať:
ISO 690:
Chen, X., Huo, M., Liu, Y. 2012. MST-Based Semi-Supervised Clustering Using M-Labeled Objects. In Computing and Informatics, vol. 31, no.6+, pp. 1557-1574. 1335-9150.

APA:
Chen, X., Huo, M., Liu, Y. (2012). MST-Based Semi-Supervised Clustering Using M-Labeled Objects. Computing and Informatics, 31(6+), 1557-1574. 1335-9150.