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An Efficient Algorithm for Mining Quantitative Association Rules to Raise Reliance of Data in Large Databases

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.