document: Fast mining of high dimensional expressive contrast patterns using zero-suppressed binary decision diagrams Patterns of contrast are a very important way of comparing multi-dimensional datasets . Such patterns are able to capture regions of high difference between two classes of data , and are useful for human experts and the construction of classifiers . However , mining such patterns is particularly challenging when the number of dimensions is large . This paper describes a new technique for mining several varieties of contrast pattern , based on the use of Zero-Suppressed Binary Decision Diagrams ( ZBDDs ) , a powerful data structure for manipulating sparse data . We study the mining of both simple contrast patterns , such as emerging patterns , and more novel and complex contrasts , which we call disjunctive emerging patterns . A performance study demonstrates our ZBDD technique is highly scalable , substantially improves on state of the art mining for emerging patterns and can be effective for discovering complex contrasts from datasets with thousands of attributes .
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