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International Conference on Mining Intelligence and Knowledge Exploration

MIKE 2017: Mining Intelligence and Knowledge Exploration pp 371–380Cite as

A Graph-Based Frequent Sequence Mining Approach to Text Compression

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10682))

Abstract

A novel algorithm for mining sequential patterns using a graph and multi-layered compression is the main focus of the paper. Mining for patterns is done using a graph structure which allows fast and efficient mining of necessary frequent patterns in the text. These patterns are used with a modification of the seminal LZ78 algorithm to improve the efficiency of compression. Arithmetic coding is done on top of LZ78 to further reduce the redundancy in the text and to achieve higher rates of compression. The proposed approach has been tested with standard corpora and it shows promising results in comparison with Arithmetic coding.

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References

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Indian Institute of Information Technology, Design and Manufacturing Kancheepuram, Chennai, India

    C. Oswald & B. Sivaselvan

  2. Department of Computer Science and Engineering, Sona College of Technology, Salem, India

    I. Ajith Kumar & J. Avinash

Authors
  1. C. Oswald
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  2. I. Ajith Kumar
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  3. J. Avinash
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  4. B. Sivaselvan
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Corresponding author

Correspondence to J. Avinash .

Editor information

Editors and Affiliations

  1. Indian Statistical Institute, Kolkata, India

    Ashish Ghosh

  2. Institute for Development and Research in Banking Technology, Hyderabad, India

    Rajarshi Pal

  3. Indian Institute of Information Technology, Sri City, India

    Rajendra Prasath

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Oswald, C., Ajith Kumar, I., Avinash, J., Sivaselvan, B. (2017). A Graph-Based Frequent Sequence Mining Approach to Text Compression. In: Ghosh, A., Pal, R., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2017. Lecture Notes in Computer Science(), vol 10682. Springer, Cham. https://doi.org/10.1007/978-3-319-71928-3_35

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  • DOI: https://doi.org/10.1007/978-3-319-71928-3_35

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-71927-6

  • Online ISBN: 978-3-319-71928-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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