CoBase: a scalable and extensible cooperative information system
Journal of Intelligent Information Systems - Special issue on intelligent integration of information
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Global action rules in distributed knowledge systems
Fundamenta Informaticae
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Cooperative Answering through Controlled Query Relaxation
IEEE Expert: Intelligent Systems and Their Applications
Ontology, Metadata, and Semiotics
ICCS '00 Proceedings of the Linguistic on Conceptual Structures: Logical Linguistic, and Computational Issues
Discovery of Surprising Exception Rules Based on Intensity of Implication
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Action-Rules: How to Increase Profit of a Company
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Handling Semantic Inconsistencies in Distributed Knowledge Systems Using Ontologies
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce
Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce
Action rules mining: Research Articles
International Journal of Intelligent Systems - Knowledge Discovery: Dedicated to Jan M. Żytkow
On Mining Summaries by Objective Measures of Interestingness
Machine Learning
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Action rules introduced in [12] and extended further to e-action rules [21 have been investigated in [22], [13], [20]. They assume that attributes in a database are divided into two groups: stable and flexible. In general, an action rule can be constructed from two rules extracted earlier from the same database. Furthermore, we assume that these two rules describe two different decision classes and our goal is to re-classify objects from one of these classes into the other one. Flexible attributes are essential in achieving that goal since they provide a tool for making hints to a user what changes within some values of flexible attributes are needed for a given set of objects to re-classify them into a new decision class. There are two aspects of interestingness of rules that have been studied in data mining literature, objective and subjective measures [8], [1], [14], [15], [23]. In this paper we focus on a cost of an action rule which was introduced in [22] as an objective measure. An action rule was called interesting if its cost is below and support higher than some user-defined threshold values. We assume that our attributes are hierarchical and we focus on solving the failing problem of interesting action rules discovery. Our process is cooperative and it has some similarities with cooperative answering of queries presented in [3], [5], [6].