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
Credibility coefficients based on SVM
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
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The Data Credibility Analysis is a computer science domain aimed at discovering universal algorithms for identifying improper or unusual data. It is done by calculating credibility coefficients for individual records. In recent years many different methods of computing these coefficients were presented. In the paper we propose a transformation of credibility coefficients to ordinal credibility coefficients. By developing this idea we propose another credibility coefficient computing algorithm, which benefits from incorporating arbit rary many other credibility coefficient computing methods. The preliminary tests showed that this approach leads to better results.