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
ACM Computing Surveys (CSUR)
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
On Clustering Validation Techniques
Journal of Intelligent Information Systems
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
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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
Incremental learning and concept drift in INTHELEX
Intelligent Data Analysis
Randomized metric induction and evolutionary conceptual clustering for semantic knowledge bases
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
A Web Usage Mining Framework for Mining Evolving User Profiles in Dynamic Web Sites
IEEE Transactions on Knowledge and Data Engineering
Discovering patterns in spatial data using evolutionary programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Kernel methods for mining instance data in ontologies
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Query answering and ontology population: an inductive approach
ESWC'08 Proceedings of the 5th European semantic web conference on The semantic web: research and applications
Conceptual clustering and its application to concept drift and novelty detection
ESWC'08 Proceedings of the 5th European semantic web conference on The semantic web: research and applications
Foundations of refinement operators for description logics
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
OTM'06 Proceedings of the 2006 international conference on On the Move to Meaningful Internet Systems: AWeSOMe, CAMS, COMINF, IS, KSinBIT, MIOS-CIAO, MONET - Volume Part II
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
Low-complexity fuzzy relational clustering algorithms for Web mining
IEEE Transactions on Fuzzy Systems
Combining semantic web search with the power of inductive reasoning
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
Prediction of class and property assertions on OWL ontologies through evidence combination
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
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A conceptual clustering framework is presented which can be applied to multi-relational knowledge bases storing resource annotations expressed in the standard languages for the Semantic Web. The framework adopts an effective and language-independent family of semi-distance measures defined for the space of individual resources. These measures are based on a finite number of dimensions corresponding to a committee of discriminating features represented by concept descriptions. The clustering algorithm expresses the possible clusterings in terms of strings of central elements (medoids, w.r.t. the given metric) of variable length. The method performs a stochastic search in the space of possible clusterings, exploiting a technique based on genetic programming. Besides, the number of clusters is not necessarily required as a parameter: a natural number of clusters is autonomously determined, since the search spans a space of strings of different length. An experimentation with real ontologies proves the feasibility of the clustering method and its effectiveness in terms of standard validity indices. The framework is completed by a successive phase, where a newly constructed intensional definition, expressed in the adopted concept language, can be assigned to each cluster. Finally, two possible extensions are proposed. One allows the induction of hierarchies of clusters. The other applies clustering to concept drift and novelty detection in the context of ontologies.