Algorithms and data structures: with applications to graphics and geometry
Algorithms and data structures: with applications to graphics and geometry
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
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Implementing database operations using SIMD instructions
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
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
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast computation of database operations using graphics processors
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Cache-conscious frequent pattern mining on a modern processor
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Optimizing bitmap indices with efficient compression
ACM Transactions on Database Systems (TODS)
An implementation of the FP-growth algorithm
Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
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The association rule mining, one of the most popular data mining techniques, is to find the frequent itemsets which occur commonly in transaction database. Of the various association algorithms, the Apriori is the most popular one, and its implementation technique to improve the performance has been continuously developed during the past decade. In this paper, we propose a bitmap-based association rule technique, called BAR, in order to drastically improve the performance of the Apriori algorithm. Compared to the latest Apriori implementation, our approach can improve the performance by nearly up to two orders of magnitude. This gain comes mainly from the following characteristics of BAR: 1) bitmap based implementation paradigm, 2) reduction of redundant bitmap-AND operations, and 3) an efficient implementation of bitmap-AND and bit-counting operation by exploiting the advanced CPU technology, including SIMD and SW prefetching. We will describe the basic concept of BAR approach and its optimization techniques, and will show, through experimental results, how each of the above characteristics of BAR can contribute the performance improvement.