New Techniques for Data Reduction in a Database System for Knowledge Discovery Applications
Journal of Intelligent Information Systems
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
A New Version of Rough Set Exploration System
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
A new rough sets model based on database systems
Fundamenta Informaticae - Special issue on the 9th international conference on rough sets, fuzzy sets, data mining and granular computing (RSFDGrC 2003)
A comparison of rough set methods and representative inductive learning algorithms
Fundamenta Informaticae - Special issue on the 9th international conference on rough sets, fuzzy sets, data mining and granular computing (RSFDGrC 2003)
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Dynamic reducts with large stability coefficients are good candidates for decision rules generation but it is time consuming to generate them. This paper presents an algorithm dReducts using a cascading hash function to generate (F, ε)-dynamic reducts. With the cascading hash function, an F-dynamic reduct can be generated in O(m2n) time with O(mn) space where m and n are total number of attributes and total number of instances of the table. Empirical results of generating (F, ε)-dynamic reducts using five of ten most popular UCI datasets are presented and they are compared to the Rough Set Exploration System (RSES).