Hierarchical model-based clustering of relational data with aggregates

Authors:
Jianzhong Chen;Mary Shapcott;Sally McClean;Kenny Adamson
Affiliations:
University of Ulster, Newtownabbey, N.Ireland;University of Ulster, Newtownabbey, N.Ireland;University of Ulster, Coleraine, N.Ireland;University of Ulster, Newtownabbey, N.Ireland
Venue:
Proceedings of the 2004 ACM symposium on Applied computing
Year:
2004

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Abstract

This paper proposes a propositional method for hierarchical model-based clustering of relational data. We define a new type of aggregate -- frequency aggregate, which has a vector data type and can be used to record not only the observed values but also the distribution of the values of an attribute. A hierarchical agglomerative clustering algorithm with log-likelihood distance is then applied to cluster the aggregated data tentatively, and a mixture model-based method with the EM algorithm is developed to perform a further relocation clustering, in which Bayes Information Crieterion is used to determine the optimal number of clusters.