Journal of the Korean Data and Information Science Society
- Volume 19 Issue 3
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- Pages.903-911
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- 2008
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- 1598-9402(pISSN)
Comparative Study of Quantitative Data Binning Methods in Association Rule
- Choi, Jae-Ho (Department of Bioinformatics, Changwon National University) ;
- Park, Hee-Chang (Department of Statistics, Changwon National University)
- Published : 2008.08.31
Abstract
Association rule mining searches for interesting relationships among items in a given large database. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. Many data is most quantitative data. There is a need for partitioning techniques to quantitative data. The partitioning process is referred to as binning. We introduce several binning methods ; parameter mean binning, equi-width binning, equi-depth binning, clustering-based binning. So we apply these binning methods to several distribution types of quantitative data and present the best binning method for association rule discovery.