Abstract
Association rules are a class of important regularities in databases. They are found to be very useful in practical applications. However, the number of association rules discovered in a database can be huge, thus making manual inspection and analysis of the rules difficult. In this paper, we propose a new framework to allow the user to explore the discovered rules to identify those interesting ones. This framework has two components, an interestingness analysis component, and a visualization component. The interestingness analysis component analyzes and organizes the discovered rules according to various interestingness criteria with respect to the user’s existing knowledge. The visualization component enables the user to visually explore those potentially interesting rules. The key strength of the visualization component is that from a single screen, the user is able to obtain a global and yet detailed picture of various interesting aspects of the discovered rules. Enhanced with color effects, the user can easily and quickly focus his/her attention on the more interesting/useful rules.
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Liu, B., Hsu, W., Wang, K., Chen, S. (1999). Visually Aided Exploration of Interesting Association Rules. In: Zhong, N., Zhou, L. (eds) Methodologies for Knowledge Discovery and Data Mining. PAKDD 1999. Lecture Notes in Computer Science(), vol 1574. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48912-6_52
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DOI: https://doi.org/10.1007/3-540-48912-6_52
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