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Discovery of Emerging Patterns and Their Use in Classification

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2903)

Abstract

Emerging patterns are sets of items whose frequency changes significantly from one dataset to another. They are useful as a means of discovering distinctions inherently present amongst a collection datasets and have been shown to be a powerful method for constructing accurate classifiers. In this paper, we present different varieties of emerging patterns, discuss efficient techniques for their discovery and explain how they can be used in classification.

Keywords

Association Rule Test Instance Relative Support Simple Aggregation Emerge Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  1. 1.Department of Computer Science and Software EngineeringUniversity of MelbourneAustralia

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