Abstract
Recently, co-clustering algorithms are widely used in heterogeneous information networks mining, and the distance metric is still a challenging problem. Bregman divergence is used to measure the distance in traditional co-clustering algorithms, but the hierarchical structure and the feature of the entity itself are not considered. In this paper, an agglomerative hierarchical co-clustering algorithm based on Bregman divergence is proposed to learn hierarchical structure of multiple entities simultaneously. In the aggregation process, the cost of merging two co-clusters is measured by a monotonic Bregman function, integrating heterogeneous relations and features of entities. The robustness of algorithms based on different divergences is tested on synthetic data sets. Experiments on the DBLP data sets show that our algorithm improves the accuracy over existing co-clustering algorithms.
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Shen, G., Yang, W., Wang, W., Yu, M., Dong, G. (2014). Agglomerative Hierarchical Co-clustering Based on Bregman Divergence. In: Herawan, T., Ghazali, R., Deris, M. (eds) Recent Advances on Soft Computing and Data Mining. Advances in Intelligent Systems and Computing, vol 287. Springer, Cham. https://doi.org/10.1007/978-3-319-07692-8_37
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DOI: https://doi.org/10.1007/978-3-319-07692-8_37
Publisher Name: Springer, Cham
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