Attribute Level Clustering Approach to Quantitative Association Rule Mining

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Provided by: International Journal of Computer Applications
Topic: Data Management
Format: PDF
Generating rules from quantitative data has been widely studied ever since the researchers 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 proposed 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.
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