Visual analysis of user-driven association rule mining. (October 2017)

• Record Type: Journal Article
• Authors: Chen, Wei; Xie, Cong; Shang, Pingping; Peng, Qunsheng
• Is Part Of: Journal of visual languages & computing. Volume 42(2017)
• Page Start: 76
• Page End: 85
• Publication Date: 2017-10
• Subjects: Categorical data -- Association rules -- Visual analysis; Visual programming languages (Computer science) -- Periodicals; Visual programming (Computer science) -- Periodicals; Programming languages (Electronic computers) -- Semantics -- Periodicals; Langages de programmation visuelle -- Périodiques; Programmation visuelle -- Périodiques; Langages de programmation -- Sémantique -- Périodiques; Programming languages (Electronic computers) -- Semantics; Visual programming (Computer science); Visual programming languages (Computer science); Periodicals; Electronic journals; 005
• DOI: 10.1016/j.jvlc.2017.08.007
• Languages: English
• ISSNs: 1045-926X

Abstract

Abstract: Association rules have been widely used for detecting relations between attribute-value pairs of categorical datasets. Existing solutions of mining interesting association rules are based on the support-confidence theory. However, it is non-trivial for the user to understand and modify the rules or the results of intermediate steps in the mining process, because the interestingness of rules might differ largely for various tasks and users. In this paper we reinforce conventional association rule mining process by mapping the entire process into a visualization assisted loop, with which the user workload for modulating parameters and mining rules is reduced, and the mining efficiency is greatly improved. A hierarchical matrix-based visualization technique is proposed for the user to explore the measure value and the intermediate results of association rules. We also design a set of visual exploration tools to support interactively inspection and manipulation of mining process. The effectiveness and usability of our approach is demonstrated with two scenarios.