Correlated pattern mining in quantitative databases

We study mining correlations from quantitative databases and show that this is a more effective approach than mining associations to discover useful patterns. We propose the novel notion of quantitative correlated pattern (QCP), which is founded on two formal concepts, mutual information and all-confidence. We first devise a normalization on mutual information and apply it to the problem of QCP mining to capture the dependency between the attributes. We further adopt all-confidence as a quality measure to ensure, at a finer granularity, the dependency between the attributes with specific quantitative intervals. We also propose an effective supervised method that combines 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, QCoMine, to mine QCPs efficiently by utilizing normalized mutual information and all-confidence to perform bilevel pruning. We also identify the redundancy existing in the set of QCPs and propose effective techniques to eliminate the redundancy. Our extensive experiments on both real and synthetic datasets verify the efficiency of QCoMine and the quality of the QCPs. The experimental results also justify the effectiveness of our proposed techniques for redundancy elimination. To further demonstrate the usefulness and the quality of QCPs, we study an application of QCPs to classification. We demonstrate that the classifier built on the QCPs achieves higher classification accuracy than the state-of-the-art classifiers built on association rules.