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MIC Framework: An Information-Theoretic Approach to Quantitative Association Rule Mining

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MIC Framework: An Information-Theoretic Approach to Quantitative Association Rule Mining
We propose a framework, called MIC, which adopts an information-theoretic approach to address the problem of quantitative association rule mining. In our MIC framework, we first discretize the quantitative attributes. Then, we compute the normalized mutual information between the attributes to construct a graph that indicates the strong informative-relationship between the attributes. We utilize the cliques in the graph to prune the unpromising attribute sets and hence the joined intervals between these attributes. Our experimental results show that the MIC framework significantly improves the mining speed. Importantly, we are able to obtain most of the high-confidence rules and the missing rules are shown to be less interesting.
Yiping Ke, James Cheng, Wilfred Ng
Added 01 Nov 2009
Updated 01 Nov 2009
Type Conference
Year 2006
Where ICDE
Authors Yiping Ke, James Cheng, Wilfred Ng
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