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Mining Multiple Level Association Rules to Mining Multiple Level Correlations to discover Complex Patterns
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 58 - Number 22 |
| Year of Publication: 2012 |
| Authors: Mamta, Shwetank Arya, R. P. Agarwal |
10.5120/9430-3528
|
Mamta, Shwetank Arya, R. P. Agarwal . Mining Multiple Level Association Rules to Mining Multiple Level Correlations to discover Complex Patterns. International Journal of Computer Applications. 58, 22 ( November 2012), 19-24. DOI=10.5120/9430-3528
Abstract
Recently, frequent pattern mining (FPM) has become one of the most popular data mining approaches for various applications such as education, medical, farming, analysis of sale and purchase patterns etc. Apriori algorithm [11] and FP growth algorithm are working efficiently in data mining. These algorithms are typically restricted to a single concept level of hierarchy and uniform support threshold. Sometimes domain database support concept hierarchies that represent the relationships among many different concept levels. In this paper efforts are made to discover items at multiple levels of concept hierarchy. Up till now, a very few concern has been shown to this area. In this study mining multiple levels is explored and extended to mining cross levels in large database on the basis of user specified reduced support threshold constraint.
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