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Recent sequential pattern mining methods have used the minimum description length (MDL) principle to define an encoding scheme which describes an algorithm for mining the most compressing patterns in a database.
Maximum likelihood from incomplete data via the EM algorithm
A. Dempster, N. Laird, and D. Rubin · 1977
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A greedy heuristic for the set-covering problem
V. Chvátal · 1979
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Hidden Markov model induction by Bayesian model merging
A. Stolcke and S. Omohundro · 1993
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Mining sequential patterns
R. Agrawal and R. Srikant · 1995
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Mining sequential patterns: Generalizations and performance improvements
R. Srikant and R. Agrawal · 1996
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Identifying hierarchical structure in sequences: A linear-time algorithm
C. G. Nevill-Manning and I. H. Witten · 1997
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A threshold of ln n for approximating set cover
U. Feige · 1998
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The Bayesian structural EM algorithm
N. Friedman · 1998
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KDD-Cup 2000 organizers’ report: Peeling the onion
R. Kohavi, C. E. Brodley, B. Frasca, L. Mason, and Z. Zheng · 2000
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Global partial orders from sequential data
H. Mannila and C. Meek · 2000
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PrefixSpan: Mining sequential patterns efficiently by prefix-projected pattern growth
J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal, and M.-C. Hsu · 2001
Cited alongside, same era.
SPADE: An efficient algorithm for mining frequent sequences
M. J. Zaki · 2001
Cited alongside, same era.
Sequential pattern mining using a bitmap representation
J. Ayres, J. Flannick, J. Gehrke, and T. Yiu · 2002
Cited alongside, same era.
Using sequential and non-sequential patterns in predictive web usage mining tasks
B. Mobasher, H. Dai, T. Luo, and M. Nakagawa · 2002
Cited alongside, same era.
Information Theory, Inference, and Learning Algorithms
D. J. C. MacKay · 2003
Cited alongside, same era.
Sequential pattern mining and classification of patient path
N. Jay, G. Herengt, E. Albuisson, F. Kohler, and A. Napoli · 2004
Introduction to Information Retrieval
C. D. Manning, P. Raghavan, and H. Schütze · 2008
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Sequential pattern mining for protein function prediction
M. Wang, X.-Q. Shang, and Z.-H. Li · 2008
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MAPO: Mining and recommending API usage patterns
H. Zhong, T. Xie, L. Zhang, J. Pei, and H. Mei · 2009
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Robust mining of time intervals with semi-interval partial order patterns
F. Moerchen and D. Fradkin · 2010
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LIBSVM: A library for support vector machines
C.-C. Chang and C.-J. Lin · 2011
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The sequence memoizer
F. Wood, J. Gasthaus, C. Archambeau, L. James, and Y. W. Teh · 2011
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Cited alongside, same era.
BIDE: Efficient mining of frequent closed sequences
J. Wang and J. Han · 2004
Cited alongside, same era.
Markov models for identification of significant episodes
R. Gwadera, M. J. Atallah, and W. Szpankowski · 2005
Cited alongside, same era.
Discovering frequent arrangements of temporal intervals
P. Papapetrou, G. Kollios, S. Sclaroff, and D. Gunopulos · 2005
Cited alongside, same era.
Modeling interleaved hidden processes
N. Landwehr · 2008
Cited alongside, same era.
The long and the short of it: summarising event sequences with serial episodes
N. Tatti and J. Vreeken · 2012
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Frequent Pattern Mining
C. Aggarwal and J. Han · 2014
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Mining compressing sequential patterns
H. T. Lam, F. Moerchen, D. Fradkin, and T. Calders · 2014
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The Stanford CoreNLP natural language processing toolkit
C. D. Manning, M. Surdeanu, J. Bauer, J. Finkel, S. J. Bethard, and D. McClosky · 2014
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