Fast discovery of association rules
Advances in knowledge discovery and data mining
Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory
Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Hyperplane Aggregation of Dominance Decision Rules
Fundamenta Informaticae - International Conference on Soft Computing and Distributed Processing (SCDP'2002)
Hyperplane Aggregation of Dominance Decision Rules
Fundamenta Informaticae - International Conference on Soft Computing and Distributed Processing (SCDP'2002)
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The induction of rules is one of the key issues of the Rough Sets Theory (RST). Generally, this problem is equivalent to finding prime implicants of a Boolean function, which is an NP-hard combinatorial problem. In practice, the NP-hardness makes solving medium-sized and large real-life problems difficult. To counteract this we propose a new algorithm, in which representation of relations between objects are represented in the form of binary vectors. The relations considered are: indiscernibility and dominance. It is an important enhancement of the classic RST approach, in which only indiscernibility was taken into account. Evaluation of the proposed algorithm in experiments with numerous real-life data sets produced satisfactory results.