Conceptual clustering of structured objects: a goal-oriented approach
Artificial Intelligence
A Polynomial Approach to the Constructive Induction of Structural Knowledge
Machine Learning - Special issue on evaluating and changing representation
Learning in the presence of concept drift and hidden contexts
Machine Learning
On the relative expressiveness of description logics and predicate logics
Artificial Intelligence
ACM Computing Surveys (CSUR)
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Distance Induction in First Order Logic
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Relational Distance-Based Clustering
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Distances and Limits on Herbrand Interpretations
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
The Description Logic Handbook
The Description Logic Handbook
An algorithm based on counterfactuals for concept learning in the Semantic Web
Applied Intelligence
OLINDDA: a cluster-based approach for detecting novelty and concept drift in data streams
Proceedings of the 2007 ACM symposium on Applied computing
Randomized metric induction and evolutionary conceptual clustering for semantic knowledge bases
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Discovering patterns in spatial data using evolutionary programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
A refinement operator based learning algorithm for the ALC description logic
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Clustering with a genetically optimized approach
IEEE Transactions on Evolutionary Computation
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Metric-based stochastic conceptual clustering for ontologies
Information Systems
Metric-based stochastic conceptual clustering for ontologies
Information Systems
Fuzzy Clustering for Categorical Spaces
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Grouping Results of Queries to Ontological Knowledge Bases by Conceptual Clustering
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Query Results Clustering by Extending SPARQL with CLUSTER BY
OTM '09 Proceedings of the Confederated International Workshops and Posters on On the Move to Meaningful Internet Systems: ADI, CAMS, EI2N, ISDE, IWSSA, MONET, OnToContent, ODIS, ORM, OTM Academy, SWWS, SEMELS, Beyond SAWSDL, and COMBEK 2009
Fuzzy Clustering for Semantic Knowledge Bases
Fundamenta Informaticae - Methodologies for Intelligent Systems
MEC --Monitoring Clusters' Transitions
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
What is concept drift and how to measure it?
EKAW'10 Proceedings of the 17th international conference on Knowledge engineering and management by the masses
Concept drift and how to identify it
Web Semantics: Science, Services and Agents on the World Wide Web
ASPARAGUS - a system for automatic SPARQL query results aggregation using semantics
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part I
Proceedings of the 3rd Annual ACM Web Science Conference
Using Similarity-Based Approaches for Continuous Ontology Development
International Journal on Semantic Web & Information Systems
| Hi-index | 0.00 |
The paper presents a clustering method which can be applied to populated ontologies for discovering interesting groupings of resources therein. The method exploits a simple, yet effective and language-independent, semi-distance measure for individuals, that is based on their underlying semantics along with a number of dimensions corresponding to a set of concept descriptions (discriminating features committee). The clustering algorithm is a partitional method and it is based on the notion of medoids w.r.t. the adopted semi-distance measure. Eventually, it produces a hierarchical organization of groups of individuals. A final experiment demonstrates the validity of the approach using absolute quality indices. We propose two possible exploitations of these clusterings: concept formation and detecting concept drift or novelty.