Mining dependent frequent serial episodes from uncertain sequence data

Wan, L; Chen, L; Zhang, C

Mining dependent frequent serial episodes from uncertain sequence data

Wan, L Chen, L

Zhang, C

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Data Mining, ICDM, 2013, pp. 1211 - 1216
Issue Date:: 2013-12-01

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Field	Value	Language
dc.contributor.author	Wan, L	en_US
dc.contributor.author	Chen, L https://orcid.org/0000-0002-6468-5729	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.date.issued	2013-12-01	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Data Mining, ICDM, 2013, pp. 1211 - 1216	en_US
dc.identifier.issn	1550-4786	en_US
dc.identifier.uri	http://hdl.handle.net/10453/27526
dc.description.abstract	In this paper, we focus on the problem of mining Probabilistic Dependent Frequent Serial Episodes (P-DFSEs) from uncertain sequence data. By observing that the frequentness probability of an episode in an uncertain sequence is a Markov Chain imbeddable variable, we first propose an Embeded Markov Chain-based algorithm that efficiently computes the frequentness probability of an episode by projecting the probability space into a set of limited partitions. To further improve the computation efficiency, we devise an optimized approach that prunes candidate episodes early by estimating the upper bound of their frequentness probabilities. © 2013 IEEE.	en_US
dc.relation	http://purl.org/au-research/grants/arc/LP120100566
dc.relation.ispartof	Proceedings - IEEE International Conference on Data Mining, ICDM	en_US
dc.relation.isbasedon	10.1109/ICDM.2013.35	en_US
dc.title	Mining dependent frequent serial episodes from uncertain sequence data	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Dallas, TX, USA	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

In this paper, we focus on the problem of mining Probabilistic Dependent Frequent Serial Episodes (P-DFSEs) from uncertain sequence data. By observing that the frequentness probability of an episode in an uncertain sequence is a Markov Chain imbeddable variable, we first propose an Embeded Markov Chain-based algorithm that efficiently computes the frequentness probability of an episode by projecting the probability space into a set of limited partitions. To further improve the computation efficiency, we devise an optimized approach that prunes candidate episodes early by estimating the upper bound of their frequentness probabilities. © 2013 IEEE.

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http://hdl.handle.net/10453/27526