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
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 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 association rules with adjustable accuracy
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Predictive data mining: a practical guide
Predictive data mining: a practical guide
Mining Optimized Association Rules with Categorical and Numeric Attributes
IEEE Transactions on Knowledge and 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 Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Efficient online mining of large databases
International Journal of Business Information Systems
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Recently, people have started to apply association rules to the items that exist in Large Database and have quantitative attribute, and researches on the said application are being introduced. This paper presents an efficient method to raise reliance of Large Interval Itemsets when we create Large Interval Itemsets to convert quantitative item into binary item. The presented method does not leave behind meaningful items because it creates Large Interval Itemsets centering around the 'mode'. And the method can create more quantity of minute Large Interval Itemsets and can minimize the loss of attribution of original data because it forms merged interval which is close to the figure of Minimum Support appointed by the user; Therefore, it raises reliance of data and those data will be useful when we create association rules later. Besides, it has been proved to be superior to the existing methods through the actual performance test with the real-life data such as population census data.