Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Parallel Algorithms for Discovery of Association Rules
Data Mining and Knowledge Discovery
Online Generation of Association Rules
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
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
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In this paper we propose an approach for mining association rules in large, dense databases. For finding such rules, frequent itemsets must first be discovered. As finding all the frequent itemsets is very time-consuming for dense databases, we propose an algorithm that is able to quickly discover an image of the complete set containing all the frequent itemsets. We define what an image is, and we present a genetic algorithm for discovering such an image. To monitor the discovery process we introduce the notion of dynamics of the algorithm. To measure the performances of our frequent itemsets discovery algorithm, we introduce the notion of efficiency of the discovery process.