Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Relational Data Mining
Multi-Relational Data Mining, Using UML for ILP
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Special issue on granular computing and data mining
International Journal of Intelligent Systems - Granular Computing and Data Mining
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Classification, Clustering, and Data Mining Applications: Proceedings of the Meeting of the International Federation of Classification Societies (IFCS), ... Data Analysis, and Knowledge Organization)
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
Handbook of Granular Computing
Handbook of Granular Computing
Rough Granular Computing in Knowledge Discovery and Data Mining
Rough Granular Computing in Knowledge Discovery and Data Mining
Research on Multi-Relational Classification Approaches
CINC '09 Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing - Volume 01
Editorial: Introduction to special issues on data mining and granular computing
International Journal of Approximate Reasoning
Pattern Recognition Letters
Similarity-Based Classification in Relational Databases
Fundamenta Informaticae
A Summary and Update of "Fuzzy Logic
GRC '10 Proceedings of the 2010 IEEE International Conference on Granular Computing
Interpretability assessment of fuzzy knowledge bases: A cointension based approach
International Journal of Approximate Reasoning
Information Sciences: an International Journal
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Modeling rough granular computing based on approximation spaces
Information Sciences: an International Journal
Toward a Theory of Granular Computing for Human-Centered Information Processing
IEEE Transactions on Fuzzy Systems
Association discovery from relational data via granular computing
Information Sciences: an International Journal
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We propose a novel framework for generating classification rules from relational data. This is a specialized version of the general framework intended for mining relational data and is defined in granular computing theory. In the framework proposed in this paper we define a method for deriving information granules from relational data. Such granules are the basis for generating relational classification rules. In our approach we follow the granular computing idea of switching between different levels of granularity of the universe. Thanks to this a granule-based relational data representation can easily be replaced by another one and thereby adjusted to a given data mining task, e.g. classification. A generalized relational data representation, as defined in the framework, can be treated as the search space for generating rules. On account of this the size of the search space may significantly be limited. Furthermore, our framework, unlike others, unifies not only the way the data and rules to be derived are expressed and specified, but also partially the process of generating rules from the data. Namely, the rules can be directly obtained from the information granules or constructed based on them.