WLAR-Viz: weighted least association rules visualization

Authors:
A. Noraziah;Zailani Abdullah;Tutut Herawan;Mustafa Mat Deris
Affiliations:
Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang Lebuhraya Tun Razak, Kuantan, Pahang, Malaysia;Department of Computer Science, Universiti Malaysia Terengganu, Kuala, Terengganu, Malaysia;Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang Lebuhraya Tun Razak, Kuantan, Pahang, Malaysia;Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Parit Raja, Batu Pahat, Johor, Malaysia
Venue:
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
Year:
2012

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Abstract

Mining weighted least association rules has been an increasing demand in data mining research. However, mining these types of rules often facing with difficulties especially in identifying which rules are really interesting. One of the alternative solutions is by applying the visualization model in those particular rules. In this paper, a model for visualizing weighted least association rules is proposed. The proposed model contains five main steps, including scanning dataset, constructing Least Pattern Tree (LP-Tree), applying Weighted Support Association Rules (WSAR*), capturing Weighted Least Association Rules (WELAR) and finally visualizing the respective rules. The results show that by using a three dimensional plots provide user friendly navigation to understand the weighted support and weighted least association rules.