Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
A threshold of ln n for approximating set cover (preliminary version)
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
A tight analysis of the greedy algorithm for set cover
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
&agr;-RST: a generalization of rough set theory
Information Sciences—Informatics and Computer Science: An International Journal
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Mining Optimized Association Rules with Categorical and Numeric Attributes
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Decision Rules by Matching New Objects Against Data Tables
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Approximate Reducts and Association Rules - Correspondence and Complexity Results
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Approximation algorithms for set cover and related problems
Approximation algorithms for set cover and related problems
Partial Covers, Reducts and Decision Rules in Rough Sets: Theory and Applications
Partial Covers, Reducts and Decision Rules in Rough Sets: Theory and Applications
On partial covers, reducts and decision rules
Transactions on rough sets VIII
Approximate boolean reasoning: foundations and applications in data mining
Transactions on Rough Sets V
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
On Minimal Rule Sets for Almost All Binary Information Systems
Fundamenta Informaticae - Half a Century of Inspirational Research: Honoring the Scientific Influence of Antoni Mazurkiewicz
Fundamenta Informaticae
Ensembles of Classifiers Based on Approximate Reducts
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P'2000)
Normalized Decision Functions and Measures for Inconsistent Decision Tables Analysis
Fundamenta Informaticae
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Partial association rules can be used for representation of knowledge, for inference in expert systems, for construction of classifiers, and for filling missing values of attributes. This 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 the greedy algorithm work, and results of theoretical and experimental study of association rules for the most part of binary information systems.