Semi-supervised clustering on information networks combines both the labeled and unlabeled data sets with an aim to improve the clustering performance. However, the existing semi-supervised clustering methods are all designed for homogeneous networks and do not deal with heterogeneous ones. In this work, we propose a semi-supervised clustering approach to analyze heterogeneous information networks, which include multi-typed objects and links and may contain more useful semantic information. The major challenge in the clustering task here is how to handle multi-relations and diverse semantic meanings in heterogeneous networks. In order to deal with this challenge, we introduce the concept of relation-path to measure the similarity between two data objects of the same type. Thereafter, we make use of the labeled information to extract different weights for all relation-paths. Finally, we propose SemiRPClus, a complete framework for semi-supervised learning in heterogeneous networks. Experimental results demonstrate the distinct advantages in effectiveness and efficiency of our framework in comparison with the baseline and some state-of-the-art approaches. © 2014 Springer International Publishing.
CITATION STYLE
Luo, C., Pang, W., & Wang, Z. (2014). Semi-supervised clustering on heterogeneous information networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8444 LNAI, pp. 548–559). Springer Verlag. https://doi.org/10.1007/978-3-319-06605-9_45
Mendeley helps you to discover research relevant for your work.