NDLI: Fast mining of high dimensional expressive contrast patterns using zero-suppressed binary decision diagrams

Content Provider	ACM Digital Library
Author	Bailey, James Loekito, Elsa
Abstract	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.
Starting Page	307
Ending Page	316
Page Count	10
File Format	PDF
ISBN	1595933395
DOI	10.1145/1150402.1150438
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2006-08-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Contrast patterns Zero-suppressed binary decision diagrams Disjunctive emerging patterns
Content Type	Text
Resource Type	Article

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Fast mining of high dimensional expressive contrast patterns using zero-suppressed binary decision diagrams