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
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Scalable parallel data mining for association rules
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
An efficient approach to discovering knowledge from large databases
DIS '96 Proceedings of the fourth international conference on on Parallel and distributed information systems
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Using a Hash-Based Method with Transaction Trimming for Mining Association Rules
IEEE Transactions on Knowledge and Data Engineering
Set-Oriented Mining for Association Rules in Relational Databases
ICDE '95 Proceedings of the Eleventh 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 Data Mining Technique for Discovering Interesting Association Rules
DEXA '97 Proceedings of the 8th International Workshop on Database and Expert Systems Applications
Using back-propagation to learn association rules for service personalization
Expert Systems with Applications: An International Journal
A novel manufacturing defect detection method using association rule mining techniques
Expert Systems with Applications: An International Journal
Application of particle swarm optimization to association rule mining
Applied Soft Computing
A new logic correlation rule for HIV-1 protease mutation
Expert Systems with Applications: An International Journal
Association rule mining using binary particle swarm optimization
Engineering Applications of Artificial Intelligence
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In this paper, we present an effective Boolean algorithm for mining association rules in large databases of sales transactions. Like the Apriori algorithm, the proposed Boolean algorithm mines association rules in two steps. In the first step, logic OR and AND operations are used to compute frequent itemsets. In the second step, logic AND and XOR operations are applied to derive all interesting association rules based on the computed frequent itemsets. By only scanning the database once and avoiding generating candidate itemsets in computing frequent itemsets, the Boolean algorithm gains a significant performance improvement over the Apriori algorithm. We propose two efficient implementations of the Boolean algorithm, the BitStream approach and the Sparse-Matrix approach. Through comprehensive experiments, we show that both the BitStream approach and the Sparse-Martrix approach outperform the Apriori algorithm in all database settings. Especially, the Sparse-Matrix approach shows a very significant performance improvement over the Apriori algorithm.