A new version of the rule induction system LERS
Fundamenta Informaticae
Query approximate answering system for an incomplete DKBS
Fundamenta Informaticae - Special issue: intelligent information systems
Mining generalized association rules
Future Generation Computer Systems - Special double issue on data mining
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Ontology-Driven Induction of Decision Trees at Multiple Levels of Abstraction
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
Collaborative Query Processing in DKS Controlled by Reducts
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Rough sets and boolean reasoning
Granular computing
Generalization and decision tree induction: efficient classification in data mining
RIDE '97 Proceedings of the 7th International Workshop on Research Issues in Data Engineering (RIDE '97) High Performance Database Management for Large-Scale Applications
Ontology-based distributed autonomous knowledge systems
Information Systems - Special issue on web data integration
Privacy-Preserving Data Publishing
Foundations and Trends in Databases
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This paper introduces the use of ERID [1] algorithm for classification rule discovery at various levels of granularity. We use an incomplete information system and attribute value hierarchy to extract rules. The incomplete information system is capable of storing weighted attribute values and the domains of those attributes are organized using a hierarchical tree structure. The granularity of attribute values can be adjusted using the attribute value hierarchy. The result is then processed through ERID, which is designed to discover rules from partially incomplete information systems. The capability of handling incomplete data enables to build more specific and general classification rules.