Efficient Mining of Jumping Emerging Patterns with Occurrence Counts for Classification

Łukasz Maciej Kobyliński , Krzysztof Walczak

Abstract

In this paper we propose an efficient method of discovering Jumping Emerging Patterns with Occurrence Counts for the use in classification of data with numeric or nominal attributes. This new extension of Jumping Emerging Patterns proved to perform well when classifying image data and here we experimentally compare it to other methods, by using generalized border-based pattern mining algorithm to build the classifier.
Author Łukasz Maciej Kobyliński II
Łukasz Maciej Kobyliński,,
- The Institute of Computer Science
, Krzysztof Walczak II
Krzysztof Walczak,,
- The Institute of Computer Science
Pages73-88
Book Peters James F., Skowron Andrzej, Chan Chien-Chung, Grzymała-Busse Jerzy W., Ziarko Wojciech P (eds.): Transactions on Rough Sets XIII , Lecture Notes In Computer Science, vol. 6499, 2011, Springer-Verlag, ISBN 978-3-642-18301-0, 275 p., DOI:10.1007/978-3-642-18302-7
Keywords in Englishdata mining, emerging patterns, image representation, classification.
DOIDOI:10.1007/978-3-642-18302-7_5
projectDevelopment of new methods and algorithms in the following areas: computer graphics, artificial intelligence, and information systems; and distributed systems. Project leader: Rybiński Henryk, , Phone: +48 22 234 7731, start date 24-06-2010, planned end date 31-12-2010, end date 30-11-2011, II/2010/DS/1, Completed
WEiTI Działalność statutowa
Languageen angielski
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Score (nominal)5
Citation count*5 (2014-09-02)
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