Inferring decision trees using the minimum description length principle
Information and Computation
Concept formation in structured domains
Concept formation knowledge and experience in unsupervised learning
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Approaches to parallel graph-based knowledge discovery
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Mining the Web's Link Structure
Computer
An Empirical Study of Domain Knowledge and Its Benefits to Substructure Discovery
IEEE Transactions on Knowledge and Data Engineering
IEEE Intelligent Systems
Scalable Discovery of Informative Structural Concepts Using Domain Knowledge
IEEE Expert: Intelligent Systems and Their Applications
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Discovery of Inexact Concepts from Structural Data
IEEE Transactions on Knowledge and Data Engineering
Discovering Structural Patterns in Telecommunications Data
Proceedings of the Thirteenth International Florida Artificial Intelligence Research Society Conference
Structural Knowledge Discovery Used to Analyze Earthquake Activity
Proceedings of the Thirteenth International Florida Artificial Intelligence Research Society Conference
Graph-Based Hierarchical Conceptual Clustering
Proceedings of the Thirteenth International Florida Artificial Intelligence Research Society Conference
A Lattice-Based Approach to Hierarchical Clustering
Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference
Finding Reusable UML Sequence Diagrams Automatically
IEEE Software
Finding Frequent Patterns in a Large Sparse Graph*
Data Mining and Knowledge Discovery
Graph-based Relational Learning with Application to Security
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
A platform based on the multi-dimensional data modal for analysis of bio-molecular structures
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Combinatorial optimization in system configuration design
Automation and Remote Control
K-means clustering versus validation measures: a data-distribution perspective
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
WSEAS Transactions on Information Science and Applications
Molecular dynamics-like data clustering approach
Pattern Recognition
Hierarchical comments-based clustering
Proceedings of the 2011 ACM Symposium on Applied Computing
Mining usage patterns from a repository of scientific workflows
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Graph-based Relational Learning with Application to Security
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
Towards hierarchical clustering
CSR'07 Proceedings of the Second international conference on Computer Science: theory and applications
Assessing the quality of multilevel graph clustering
Data Mining and Knowledge Discovery
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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.