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Graph-based hierarchical conceptual clustering (2001).
| Content Provider | CiteSeerX |
|---|---|
| Author | Jonyer, Istvan Cook, Diane J. Holder, Lawrence B. |
| 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. |
| File Format | |
| Publisher Date | 2001-01-01 |
| Access Restriction | Open |
| Subject Keyword | Graph-based Hierarchical Conceptual Clustering Data Mining Technique Substructure Discovery Technique Cobweb Clustering Algorithm Structural Information Unstructured Domain Hierarchical Clustering Hierarchical Conceptual Clustering Clustering Functionality Knowledge Discovery Subdue Substructure Discovery System Structurally-defined Clustering Graph-based Representation Several Example Unstructured Data |
| Content Type | Text |
