Clustering Relational Data Using Attribute and Link Information

J. Neville, M. Adler and D. Jensen (2003). Clustering relational data using attribute and link information. Proceedings of the Text Mining and Link Analysis Workshop, Eighteenth International Joint Conference on Artificial Intelligence.

Abstract

Clustering is a descriptive task that seeks to identify natural groupings in data. Relational data offer a wealth of information for identifying groups of similar items. Both attribute information and the structure of relationships can be used for clustering. Graph partitioning and data clustering techniques can be applied independently to relational data but a technique that exploits both sources of information simultaneously may produce more meaningful clusters. This paper will describe our work synthesizing data clustering and graph partitioning techniques into improved clustering algorithms for relational data.

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