KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Applied Mathematics and Computational Science Program
Extensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Group
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AbstractThis paper is devoted to the study of approximate algorithms for minimization of partial association rule length. It is shown that under some natural assumptions on the class NP, a greedy algorithm is close to the best polynomial approximate algorithms for solving of this NP-hard problem. The paper contains various bounds on precision of the greedy algorithm, bounds on minimal length of rules based on an information obtained during greedy algorithm work, and results of the study of association rules for the most part of binary information systems. © 2009 Springer Berlin Heidelberg.
PublisherSpringer Science + Business Media
Conference/Event name4th International Conference on Rough Sets and Knowledge Technology, RSKT 2009