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Graph-Based Hierarchical Conceptual Clustering (2000)
| Content Provider | CiteSeerX |
|---|---|
| Author | Holder, Lawrence B. Cook, Diane J. Jonyer, Istvan |
| Abstract | Hierarchical conceptual clustering has been proven to be a useful data mining technique. Graph-based representation of structural information has been shown to be successful in knowledge discovery. The Subdue substructure discovery system provides the advantages of both approaches. In this paper we present Subdue and focus on its clustering capabilities. We use two examples to illustrate the validity of the approach both in structured and unstructured domains, as well as compare Subdue to an earlier clustering algorithm. |
| File Format | |
| Journal | International Journal on Artificial Intelligence Tools |
| Publisher Date | 2000-01-01 |
| Access Restriction | Open |
| Subject Keyword | Clustering Capability Graph-based Representation Useful Data Mining Technique Present Subdue Compare Subdue Knowledge Discovery Graph-based Hierarchical Conceptual Clustering Structural Information Subdue Substructure Discovery System Clustering Algorithm Hierarchical Conceptual Clustering Unstructured Domain |
| Content Type | Text |
| Resource Type | Article |
