Attribute Level Clustering Approach to Quantitative Association Rule Mining

M. PhaniKrishnaKishore, Ashok Kumar Madamsetti
2014 International Journal of Computer Applications  
Generating rules from quantitative data has been widely studied ever since Agarwal and Srikanth explored the problem through their works on association rule mining. Discretization of the ranges of the attributes has been one of the challenging tasks in quantitative association rule mining that guides the rules generated. Also several algorithms are being proposed for fast identification of frequent item sets from large data sets. In this paper a new data driven partitioning algorithm has been
more » ... oposed to discretize the ranges of the attributes. Also a new approach has been presented to create meta data for the given data set from which frequent item sets can be generated quickly for any given support counts.
doi:10.5120/16598-6404 fatcat:ftsbtd3annc7tf75ghtkjtlnt4