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Attribute Level Clustering Approach to Quantitative Association Rule Mining

by M. Phani Krishna Kishore, Ashok Kumar Madamsetti
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 95 - Number 6
Year of Publication: 2014
Authors: M. Phani Krishna Kishore, Ashok Kumar Madamsetti
10.5120/16598-6404

M. Phani Krishna Kishore, Ashok Kumar Madamsetti . Attribute Level Clustering Approach to Quantitative Association Rule Mining. International Journal of Computer Applications. 95, 6 ( June 2014), 17-23. DOI=10.5120/16598-6404

@article{ 10.5120/16598-6404,
author = { M. Phani Krishna Kishore, Ashok Kumar Madamsetti },
title = { Attribute Level Clustering Approach to Quantitative Association Rule Mining },
journal = { International Journal of Computer Applications },
issue_date = { June 2014 },
volume = { 95 },
number = { 6 },
month = { June },
year = { 2014 },
issn = { 0975-8887 },
pages = { 17-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume95/number6/16598-6404/ },
doi = { 10.5120/16598-6404 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:18:43.390948+05:30
%A M. Phani Krishna Kishore
%A Ashok Kumar Madamsetti
%T Attribute Level Clustering Approach to Quantitative Association Rule Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 95
%N 6
%P 17-23
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

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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Index Terms

Computer Science
Information Sciences

Keywords

Quantitative association rule mining association rule mining.

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