Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Data mining using two-dimensional optimized association rules: scheme, algorithms, and visualization
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Turbo-charging vertical mining of large databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
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Association Rule represents a promising technique to find hidden patterns in database. The main issue about mining association rule in the large database. One of the most famous association rule learning algorithms is Apriori. Apriori algorithm is one of algorithms for generation of association rules. The drawback of Apriori Rule algorithm is the number of time to read data in the database equally number of each candidate were generated. Many research papers have been published trying to reduce the amount of time to read data from the database. In this paper, we propose a new algorithm that will work rapidly. Boolean Algebra and Compression technique for Association rule Mining (B-Compress) is applied to compress database and reduce the amount of times to scan database tremendously. Boolean Algebra combines, compresses, generates candidate itemset and counts the number of candidates. The construction method of B-Compress has ten times higher mining efficiency in execution time than Apriori Rule.