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
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Online algorithms for finding profile association rules
Proceedings of the seventh international conference on Information and knowledge management
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 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
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
CT-ITL: efficient frequent item set mining using a compressed prefix tree with pattern growth
ADC '03 Proceedings of the 14th Australasian database conference - Volume 17
Fast vertical mining using diffsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
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Association rule mining has been a core research topic in data mining. Most of the past researches focused on discovering relationships among items in the transaction database. In addition, mining algorithms for discovering association rules need the support threshold to discover frequent itemsets. But the essence of association rule mining is to find very associated relationships among itemsets not to discover frequent itemsets. In this paper, we deal with mining the relationships among the customer profile information and the purchased items. We make the sample databases from the original database and use the tests of hypotheses on the interestingness of the rules from the sample data. Our approach can speed up mining process by storing the sample database into main memory and provide insights by presenting the rules of low support but high association.