Article: Efficient attribute selection strategies for association rule mining in high dimensional data Journal: International Journal of Computational Science and Engineering (IJCSE) 2017 Vol.15 No.3/4 pp.201 - 213 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. Inderscience Publishers - linking academia, business and industry through research

Title: Efficient attribute selection strategies for association rule mining in high dimensional data

Authors: Sandhya Harikumar; Divya Usha Dilipkumar; M. Ramachandran Kaimal

Addresses: Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham (Amrita University), Amritapuri, India ' Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham (Amrita University), Amritapuri, India ' Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham (Amrita University), Amritapuri, India

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.

Keywords: association rule mining; apriori; entropy; QR decomposition.

DOI: 10.1504/IJCSE.2017.087416

International Journal of Computational Science and Engineering, 2017 Vol.15 No.3/4, pp.201 - 213

Received: 18 Apr 2016
Accepted: 27 Aug 2016
Published online: 15 Oct 2017 *

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Title: Efficient attribute selection strategies for association rule mining in high dimensional data

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