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Attribute Level Clustering Approach to Quantitative Association Rule Mining

International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 95 - Number 6
Year of Publication: 2014
M. Phani Krishna Kishore
Ashok Kumar Madamsetti

Phani Krishna M Kishore and Ashok Kumar Madamsetti. Article: Attribute Level Clustering Approach to Quantitative Association Rule Mining. International Journal of Computer Applications 95(6):17-23, June 2014. Full text available. BibTeX

	author = {M. Phani Krishna Kishore and Ashok Kumar Madamsetti},
	title = {Article: Attribute Level Clustering Approach to Quantitative Association Rule Mining},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {95},
	number = {6},
	pages = {17-23},
	month = {June},
	note = {Full text available}


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 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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