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2001 JMLR JMLR 2001

Graph-Based Hierarchical Conceptual Clustering

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. [abs] [pdf] [ps.gz] [ps]

📈 Trend Setter — Clustering
🧭 Keyword Pioneer — hierarchical clustering
🐝 Cross-Pollinator — Artificial Intelligence, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing
🌱 Topic Pioneer — Data Mining
🌉 Interdisciplinary Bridge — Data Science & Analytics and Machine Learning
🐣 Hot Topic Early Bird — hierarchical clustering

Authors

Istvan Jonyer , Diane J. Cook , Lawrence B. Holder

Topics

Machine Learning > Core Methods > Clustering Data Science & Analytics > Methods > Data Mining Data Science & Analytics > Applications > Clustering

Keywords

hierarchical clustering graph-based clustering data mining knowledge discovery conceptual clustering substructure discovery
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