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Attribute Level Clustering Approach to Quantitative Association Rule Mining
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
doi:10.5120/16598-6404
fatcat:ftsbtd3annc7tf75ghtkjtlnt4