Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
CAEP: Classification by Aggregating Emerging Patterns
DS '99 Proceedings of the Second International Conference on Discovery Science
Credibility coefficients in ARES rough set exploration system
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
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ARES System is an application dedicated to data analysis supported by Rough Set theory. Currently the system is expanded by such approaches as Emerging Patterns and Support Vector Machine. A unique feature of ARES System is applying credibility coefficients to identify improper objects within information systems. The credibility coefficient is a measure, which attempts to assess a degree of typicality of each object in respect to the rest of information system. The paper presents a concept of credibility coefficients in context of hybrid artificial intelligence systems combined on ARES System platform. Ordinal credibility coefficient supports aggregation of number incomparable credibility coefficients based on different approaches.