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Maximal Words in Sequence Comparisons Based on Subword Composition

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Algorithms and Applications

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6060))

Abstract

Measures of sequence similarity and distance based more or less explicitly on subword composition are attracting an increasing interest driven by intensive applications such as massive document classification and genome-wide molecular taxonomy. A uniform character of such measures is in some underlying notion of relative compressibility, whereby two similar sequences are expected to share a larger number of common substrings than two distant ones. This paper reviews some of the approaches to sequence comparison based on subword composition and suggests that their common denominator may ultimately reside in special classes of subwords, the nature of which resonates in interesting ways with the structure of popular subword trees and graphs.

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Author information

Authors and Affiliations

  1. Georgia Institute of Technology & Università di Padova,  

    Alberto Apostolico

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  1. Alberto Apostolico
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Editor information

Editors and Affiliations

  1. Department of Software Systems, Tampere University of Technology, P. O. Box 553, 33101, Tampere, Finland

    Tapio Elomaa

  2. Department of Information and Computer Science, Aalto University School of Science and Technology, P.O. Box 17800, 00076, Aalto, Finland

    Heikki Mannila

  3. Department of Information and Computer Science, Aalto University School of Science and Technology, P.O. Box 15400, 00076, Aalto, Finland

    Pekka Orponen

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Apostolico, A. (2010). Maximal Words in Sequence Comparisons Based on Subword Composition. In: Elomaa, T., Mannila, H., Orponen, P. (eds) Algorithms and Applications. Lecture Notes in Computer Science, vol 6060. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12476-1_2

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  • DOI: https://doi.org/10.1007/978-3-642-12476-1_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12475-4

  • Online ISBN: 978-3-642-12476-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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