Graph-Based Hierarchical Conceptual Clustering
Istvan Jonyer, Diane J. Cook, Lawrence B. Holder;
2(Oct):19-43, 2001.
Abstract
Hierarchical conceptual clustering has proven to be a useful, although under-explored, data mining
technique. A graph-based representation of structural information combined with a substructure
discovery technique has been shown to be successful in knowledge discovery. The SUBDUE
substructure discovery system provides one such combination of approaches. This work presents
SUBDUE and the development of its clustering functionalities. Several examples are used to
illustrate the validity of the approach both in structured and unstructured domains, as well as to
compare SUBDUE to the Cobweb clustering algorithm. We also develop a new metric for
comparing structurally-defined clusterings. Results show that SUBDUE successfully discovers
hierarchical clusterings in both structured and unstructured data.
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