Conceptual clustering in a first order logic representation
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Object-oriented analysis and design with applications (2nd ed.)
Object-oriented analysis and design with applications (2nd ed.)
A Polynomial Approach to the Constructive Induction of Structural Knowledge
Machine Learning - Special issue on evaluating and changing representation
Generalization-based data mining in object-oriented databases using an object cube model
Data & Knowledge Engineering - Special jubilee issue: DKE 25
A Conceptual Clustering Algorithm for Database Schema Design
IEEE Transactions on Knowledge and Data Engineering
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Dissimilarity Measure for Collections of Objects and Values
IDA '97 Proceedings of the Second International Symposium on Advances in Intelligent Data Analysis, Reasoning about Data
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The paper deals with clustering of objects described both by properties and relations. Relational attributes may make object descriptions recursively depend on themselves so that attribute values cannot be compared before objects themselves are. An approach to clustering is presented whose core element is an object dissimilarity measure. All sorts of object attributes are compared in a uniform manner with possible exploration of the existing taxonomic knowledge. Dissimilarity values for mutually dependent object couples are computed as solutions of a system of linear equations. An example of building classes on objects with self-references demonstrates the advantages of the suggested approach.