Jumping emerging patterns with occurrence count in image classification

Authors:
Łukasz Kobyliński;Krzysztof Walczak
Affiliations:
Institute of Computer Science, Warsaw University of Technology, Warszawa, Poland;Institute of Computer Science, Warsaw University of Technology, Warszawa, Poland
Venue:
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2008

Citing 2
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Efficient Mining of Jumping Emerging Patterns with Occurrence Counts for Classification

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Abstract

In this paper we propose an application of jumping emerging patterns (JEPs) to the classification of images. We define of a new type of patterns, namely the jumping emerging patterns with occurrence count (occJEPs), which allow reasoning in transaction databases with recurrent items. Such data is a frequently used representation of images, for which classification is one of the most important data mining problems that needs to be solved accurately and efficiently. We provide a formal definition of the new type of patterns, an outline of an algorithm for finding occJEPs and a comparison with other rule- and pattern-based classifiers for a selection of sample images.