Definition
Graph clustering refers to clustering of data in the form of graphs. Two distinct forms of clustering can be performed on graph data. Vertex clustering seeks to cluster the nodes of the graph into groups of densely connected regions based on either edge weights or edge distances. The second form of graph clustering treats the graphs as the objects to be clustered and clusters these objects on the basis of similarity. The second approach is often encountered in the context of structured or XML data.
Motivation and Background
Graph clustering is a form of graph mining that is useful in a number ofpractical applications including marketing, customer segmentation, congestiondetection, facility location, and XML data integration (Lee, Hsu, Yang, &Yang, 2002). The graph clustering problems are typically defined into twocategories:
Node clustering algorithms: Node clustering algorithms are...
References
Abello, J., Resende, M. G., & Sudarsky, S. (2002). Massive quasi-clique detection. In Proceedings of the 5th Latin American symposium on theoretical informatics (LATIN) (pp. 598–612). Berlin: Springer.
Aggarwal, C., Ta, N., Feng, J., Wang, J., & Zaki, M. J. (2007). XProj: A framework for projected structural clustering of XML documents. In KDD conference (pp. 46–55). San Jose, CA.
Ahuja, R., Orlin, J., & Magnanti, T. (1992). Network flows: Theory, algorithms, and applications. Englewood Cliffs, NJ: Prentice-Hall.
Chawathe, S. S. (1999). Comparing hierachical data in external memory. In Very large data bases conference (pp. 90–101). San Francisco: Morgan Kaufmann.
Chung, F. (1997). Spectral graph theory. Washington, DC: Conference Board of the Mathematical Sciences.
Dalamagas, T., Cheng, T., Winkel, K., & Sellis, T. (2005). Clustering XML documents using structural summaries. In Information systems. Elsevier, January 2005.
Gibson, D., Kumar, R., & Tomkins, A. ( ). Discovering large dense subgraphs in massive graphs. In VLDB conference (pp. 721-732). http://www.vldb2005.org/program/paper/thu/p721-gibson.pdf
Jain, A., & Dubes, R. (1998). Algorithms for clustering data. Englewood, NJ: Prentice-Hall.
Kernighan, B. W., & Lin, S. (1970). An efficient heuristic procedure for partitioning graphs, Bell System Technical Journal, 49, 291–307.
Lee, M., Hsu, W., Yang, L., & Yang, X. (2002). XClust: Clustering XML schemas for effective integration. In ACM conference on information and knowledge management. http://doi.acm.org/10.1145/584792.584841
Lian, W., Cheung, D. W., Mamoulis, N., & Yiu, S. (2004). An efficient and scalable algorithm for clustering XML documents by structure, IEEE Transactions on Knowledge and Data Engineering, 16(1), 82–96.
Pei, J., Jiang, D., & Zhang, A. (2005). On mining cross-graph quasi-cliques. In ACM KDD conference. Chicago, IL.
Rattigan, M., Maier, M., & Jensen, D. (2007). Graph clustering with network structure indices. Proceedings of the International Conference on Machine Learning (783-790). ACM: New York.
Tsay, A. A., Lovejoy, W. S., & Karger, D. R. (1999). Random sampling in cut, flow, and network design problems. Mathematics of Operations Research, 24(2), 383–413.
Zeng, Z., Wang, J., Zhou, L., & Karypis, G. (2007). Out-of-core coherent closed quasi-clique mining from large dense graph databases. ACM Transactions on Database Systems, 32(2), 13.
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Aggarwal, C.C. (2011). Graph Clustering. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_348
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DOI: https://doi.org/10.1007/978-0-387-30164-8_348
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