document: 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 , QCoMine , to efficiently mine QCPs by utilizing normalized mutual information and all-confidence to perform a two-level pruning . Our experiments verify the efficiency of QCoMine and the quality of the QCPs .
doc_bio_tags: O O B I O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B I O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O