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
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
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Mining frequent patterns with counting inference
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Towards long pattern generation in dense databases
ACM SIGKDD Explorations Newsletter
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Pushing Support Constraints Into Association Rules Mining
IEEE Transactions on Knowledge and Data Engineering
Mining Top.K Frequent Closed Patterns without Minimum Support
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
A fast high utility itemsets mining algorithm
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Proportional fault-tolerant data mining with applications to bioinformatics
Information Systems Frontiers
Incremental itemset mining based on matrix apriori algorithm
DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery
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This work discusses the problem of generating association rules from a set of transactions in a relational database, taking performance and accuracy of found results as the essential aspects for comparing association mining algorithms. We do a critical analysis of two previously existing methods, Apriori and FP-growth, emphasizing their strengths and weaknesses; and based on this analysis, we propose an algorithm called Matrix Apriori combining the best features of both.Matrix Apriori utilizes simple structures such as matrices and vectors in the process of generating frequent patterns, and it also minimizes the number of candidate sets, thus achieving a more efficient computation than Apriori and FP-growth. The proposed algorithm can be easily extended to incorporate multiple minimal support defined by the user with the aim of improving method efficacy.