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

@inproceedings{citeulike:1876423, abstract = {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.}, author = {Ke, Yiping and Cheng, J. and Ng, W.}, booktitle = {Data Engineering, 2006. ICDE '06. Proceedings of the 22nd International Conference on}, citeulike-article-id = {1876423}, citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1617480}, journal = {Data Engineering, 2006. ICDE '06. Proceedings of the 22nd International Conference on}, pages = {112}, posted-at = {2007-11-07 05:50:24}, priority = {2}, title = {MIC Framework: An Information-Theoretic Approach to Quantitative Association Rule Mining}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1617480}, year = {2006} }

TY - CONF ID - citeulike:1876423 L3 - citeulike-article-id:1876423 N2 - 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. JF - Data Engineering, 2006. ICDE '06. Proceedings of the 22nd International Conference on EP - 112 TI - MIC Framework: An Information-Theoretic Approach to Quantitative Association Rule Mining T2 - Data Engineering, 2006. ICDE '06. Proceedings of the 22nd International Conference on SP - 112 AU - Ke, Yiping AU - Cheng, J AU - Ng, W PY - 2006/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1617480 ER -