Abstract
This paper suggests an efficient method to raise reliance of Large Interval Itemsets to convert quantitative item into binary item. The suggested method does not leave behind meaningful items. And can create more quantity of minute Large Interval Itemsets and can minimize the loss of attribution of original data because it generate merged interval which is close to the figure of Minimum Support appointed by the user and generate Large Interval Itemsets under the consideration of characteristic of data-occurrence Therefore, it raises reliance of data and those data will be useful when we create association rules later.
This work was supported by grant No. R05-2002-000-00128-0(2003) form Korea Science & Engineering Foundation
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Lee, HJ., Park, WH., Park, DS. (2004). An Efficient Method for Quantitative Association Rules to Raise Reliance of Data. In: Yu, J.X., Lin, X., Lu, H., Zhang, Y. (eds) Advanced Web Technologies and Applications. APWeb 2004. Lecture Notes in Computer Science, vol 3007. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24655-8_55
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DOI: https://doi.org/10.1007/978-3-540-24655-8_55
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