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  4. Linear space direct pattern sampling using coupling from the past
 
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2012
Conference Paper
Title

Linear space direct pattern sampling using coupling from the past

Abstract
This paper shows how coupling from the past (CFTP) can be used to avoid time and memory bottlenecks in direct local pattern sampling procedures. Such procedures draw controlled amounts of suitably biased samples directly from the pattern space of a given dataset in polynomial time. Previous direct pattern sampling methods can produce patterns in rapid succession after some initial preprocessing phase. This preprocessing phase, however, turns out to be prohibitive in terms of time and memory for many datasets. We show how CFTP can be used to avoid any super-linear preprocessing and memory requirements. This allows to simulate more complex distributions, which previously were intractable. We show for a large number of public real-world datasets that these new algorithms are fast to execute and their pattern collections outperform previous approaches both in unsupervised as well as supervised contexts.
Author(s)
Boley, Mario  
Moens, S.
Gärtner, Thomas  
Mainwork
KDD '12. Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. CD-ROM  
Conference
International Conference on Knowledge Discovery and Data Mining (KDD) 2012  
DOI
10.1145/2339530.2339545
Language
English
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
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