Dynamic Mining of Quantitative and Categorical Attributes with Skewed Support Distribution

Authors:
M. R. Karthik;C. P. Saravana Kumar
Affiliations:
Dept. of Electrical and Electronics Eng, PSG College of Technology, Coimbatore, India, karthikv2k@yahoo.co.uk;Dept. of Electrical and Electronics Eng, PSG College of Technology, Coimbatore, India, saravana_cp@yahoo.com
Venue:
Proceedings of the 2006 conference on Advances in Intelligent IT: Active Media Technology 2006
Year:
2006

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Abstract

Though many algorithms have focused on mining quantitative attributes with a uniform support distribution, very little work is done in mining quantitative attributes with skewed support distribution. Still the binning methods in these algorithms are not effective against skewed support distribution and noise data; mostly they are not dynamic. In this paper, two efficient algorithms are proposed: Support-distribution Based Binning (SBB) for binning quantitative attributes and Dynamic Rule Mining (DRM) for association rule computation. The performance of these algorithms is experimented with real-life data sets and results are analyzed.