Abstract
We have reviewed several state-of-the-art machine learning approaches to different types of link-based clustering in this chapter. Specifically, we have presented the spectral clustering for heterogeneous relational data, the symmetric convex coding for homogeneous relational data, the citation model for clustering the special but popular homogeneous relational data—the textual documents with citations, the probabilistic clustering framework on mixed membership for general relational data, and the statistical graphical model for dynamic relational clustering. We have demonstrated the effectiveness of these machine learning approaches through empirical evaluations.
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Notes
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Each state is represented as a distinct cluster.
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Acknowledgments
This work is supported in part through NSF grants [IIS-0535162, IIS-0812114, IIS-0905215, and DBI-0960443], as well as graduate research internships at Google Research Labs and NEC Laboratories America, Inc. Yun Chi, Yihong Gong, Xiaoyun Wu, and Shenghuo Zhu have made contributions to part of this material.
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Zhang, Z.(., Long, B., Guo, Z., Xu, T., Yu, P.S. (2010). Machine Learning Approaches to Link-Based Clustering. In: Yu, P., Han, J., Faloutsos, C. (eds) Link Mining: Models, Algorithms, and Applications. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-6515-8_1
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