Efficient attribute selection strategies for association rule mining in high dimensional data
by Sandhya Harikumar; Divya Usha Dilipkumar; M. Ramachandran Kaimal
International Journal of Computational Science and Engineering (IJCSE), Vol. 15, No. 3/4, 2017

Abstract: This paper presents a new computational approach to discover interesting relations between variables, called association rules, in large and high dimensional datasets. State-of-the-art techniques are computationally expensive due to reasons like high dimensions, generation of huge number of candidate sets and multiple database scans. In general, most of the enormous discovered patterns are obvious, redundant or uninteresting to the user. So the context of this paper is to improve apriori algorithm to find association rules pertaining to only important attributes from high dimensional data. We employ an information theoretic method together with the concept of QR decomposition to represent the data in its proper substructure form without losing its semantics, by identifying significant attributes. Experiment on real datasets and comparison with the existing technique reveals that the proposed strategy is computationally always faster and statistically always comparable with the apriori algorithms in terms of rules generated and time complexity.

Online publication date: Sun, 15-Oct-2017

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Science and Engineering (IJCSE):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com