Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Variable precision rough set model
Journal of Computer and System Sciences
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Fast discovery of association rules
Advances in knowledge discovery and data mining
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
A fast distributed algorithm for mining association rules
DIS '96 Proceedings of the fourth international conference on on Parallel and distributed information systems
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
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Reduction of the number of attributes to calculate rules in large data-bases is of great interest in data mining. In this paper, we propose a method for reducing the number of attributes in rules using frequent item sets calculation. The method is based in a basic step model. In our approach algorithms are divided in atomic operations that have been called basic steps so that it is easier to optimize the execution of any algorithm. We also present the implementation of this approach in Damisys what demonstrates that our approach is implementable and effective dealing with large datasets.