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Cluster Computing

, Volume 22, Supplement 3, pp 5905–5917 | Cite as

Mining distinguishing subsequence patterns with nonoverlapping condition

  • Youxi Wu
  • Yuehua Wang
  • Jingyu Liu
  • Ming Yu
  • Jing Liu
  • Yan LiEmail author
Article
  • 200 Downloads

Abstract

Distinguishing subsequence patterns mining aims to discover the differences between different categories of sequence databases and to express characteristics of classes. It plays an important role in biomedicine, feature information selection, time-series classification, and other areas. The existing distinguishing subsequence patterns mining only focuses on whether a pattern appears in a sequence, regardless of the number of occurrences of the pattern in the sequence and the proportion of the pattern in the entire sequence database, which affects the discovery of the distinguishing patterns when there are a large number of irrelevant occurrences. Therefore, the nonoverlapping conditional distinguishing subsequence patterns mining algorithm is proposed. In this paper, we focus on the number of nonoverlapping occurrences that effectively reduce the number of irrelevant or redundant occurrences, and in this way, the number of occurrences can be better grasped. At the same time, we use a specially designed data structure, namely, a Nettree, to avoid backtracking. In addition, we use the distinguishing patterns as classification features, and carry out classification experiments on DNA sequences and time-series data with two classes. Extensive experimental results and comparisons demonstrate the efficiency of the proposed algorithm and the correctness of the feature extraction.

Keywords

Nonoverlapping occurrences Distinguishing subsequence pattern Nettree Feature extraction 

Notes

Acknowledgements

The work was supported in part by the National Natural Science Foundation of China under Grant 61673159, in part by the Natural Science Foundation of Hebei Province under Grant F2016202145, in part by the Science and the Technology Project of Hebei Province under Grant 15210325, and in part by the Graduate Student Innovation Program of Hebei Province under Grant CXZZSS2017037.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Youxi Wu
    • 1
    • 2
  • Yuehua Wang
    • 1
    • 2
  • Jingyu Liu
    • 1
    • 2
  • Ming Yu
    • 1
    • 2
  • Jing Liu
    • 1
    • 2
  • Yan Li
    • 3
    Email author
  1. 1.School of Computer Science and EngineeringHebei University of TechnologyTianjinChina
  2. 2.Hebei Province Key Laboratory of Big Data CalculationTianjinChina
  3. 3.School of Economics and ManagementSchool of Hebei University of TechnologyTianjinChina

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