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Random Structures and Algorithms
Article . 2013 . Peer-reviewed
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Article . 2015
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https://dx.doi.org/10.48550/ar...
Article . 2011
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Article . 2015
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Uncommon suffix tries

Authors: Cénac, Peggy; Chauvin, Brigitte; Paccaut, Frédéric; Pouyanne, Nicolas;

Uncommon suffix tries

Abstract

AbstractCommon assumptions on the source producing the words inserted in a suffix trie with n leaves lead to a height and saturation level. We provide an example of a suffix trie whose height increases faster than a power of n and another one whose saturation level is negligible with respect to . Both are built from VLMC (Variable Length Markov Chain) probabilistic sources and are easily extended to families of tries having the same properties. The first example corresponds to a “logarithmic infinite comb” and enjoys a non uniform polynomial mixing. The second one corresponds to a “factorial infinite comb” for which mixing is uniform and exponential. © 2013 Wiley Periodicals, Inc. Random Struct. Alg., 46, 117–141, 2015

Country
France
Keywords

FOS: Computer and information sciences, [MATH.MATH-PR] Mathematics [math]/Probability [math.PR], Data structures, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], variable length Markov chain, probabilistic source, suffix tree, [INFO.INFO-DS] Computer Science [cs]/Data Structures and Algorithms [cs.DS], suffix trie, 60J05, Discrete-time Markov processes on general state spaces, Computer Science - Data Structures and Algorithms, FOS: Mathematics, Data Structures and Algorithms (cs.DS), prefix tree, 000, Probability (math.PR), suffix trie., 37E05, variable-length Markov chain, 004, [MATH.MATH-PR]Mathematics [math]/Probability [math.PR], mixing properties, Mathematics - Probability

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
Green