Automated Refinement of First-Order Horn-Clause Domain Theories
Machine Learning
Tolerance approximation spaces
Fundamenta Informaticae - Special issue: rough sets
Inductive logic programming and knowledge discovery in databases
Advances in knowledge discovery and data mining
Knowledge discovery by application of rough set models
Rough set methods and applications
An extended transformation approach to inductive logic programming
ACM Transactions on Computational Logic (TOCL) - Special issue devoted to Robert A. Kowalski
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Machine Learning
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Discovery of relational association rules
Relational Data Mining
Distance based approaches to relational learning and clustering
Relational Data Mining
Propositionalization approaches to relational data mining
Relational Data Mining
Strong similarity measures for ordered sets of documents in information retrieval
Information Processing and Management: an International Journal
Term Comparisons in First-Order Similarity Measures
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Similarity measures on three kinds of fuzzy sets
Pattern Recognition Letters
Learning First-Order Rules: A Rough Set Approach
Fundamenta Informaticae - International Conference on Soft Computing and Distributed Processing (SCDP'2002)
Similarity measures of intuitionistic fuzzy sets based on L p metric
International Journal of Approximate Reasoning
RSD: relational subgroup discovery through first-order feature construction
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Similarity-Based Classification in Relational Databases
Fundamenta Informaticae
Solution of a class of Intuitionistic Fuzzy Assignment Problem by using similarity measures
Knowledge-Based Systems
Information Sciences: an International Journal
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In this paper, we propose and investigate several similarity measures on complex structured objects. The objects are understood as examples of a target relation, and they are expressed in a first-order logic language. We also propose and experimentally verify an algorithm for description and classification of objects. The algorithm transforms data expressed in the first-order logic language into a decision table expressed in an attribute-value language. The table is constructed by applying a similarity measure as well as some notions of rough sets. Decision rules induced from such a table are treated as a description of objects. The rules can also be applied for classification of new unseen objects.