Mining quantitative correlated patterns using an information-theoretic approach

Existing research on mining quantitative databases mainly focuses on mining associations. However, mining associations is too expensive to be practical in many cases. In this paper, we study mining correlations from quantitative databases and show that it is a more effective approach than mining associations. We propose a new notion of Quantitative Correlated Patterns (QCPs), which is founded on two formal concepts, mutual information and all-confidence. We first devise a normalization on mutual information and apply it to QCP mining to capture the dependency between the attributes. We further adopt all-confidence as a quality measure to control, at a finer granularity, the dependency between the attributes with specific quantitative intervals. We also propose a supervised method to combine the consecutive intervals of the quantitative attributes based on mutual information, such that the interval combining is guided by the dependency between the attributes. We develop an algorithm, Q...

• Type: Conference
• Year: 2006
• Where: KDD
• Authors: Yiping Ke, James Cheng, Wilfred Ng
• Keywords: Data Mining, KDD 2006, Mining Associations, Mining Quantitative Databases, Quantitative Attributes