Reducing computational effort for plagiarism detection with approximate string matching

Tetsuya Nakatoh, Toshiro Minami

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Currently, a large number of documents are created as digital material and distributed world-wide. Digital materials are easy to publish and copy at a remarkably low cost. As a result, many documents are copied illegally, and this practice is spreading, making plagiarism a significant social issue. Therefore, the need to develop systems that detect plagiarism is very high. We have developed a new plagiarism detection method that compares documents by using approximate string matching to detect plagiarism. We have also developed a technique that reduces the computational time of the comparison method. In this paper, we demonstrate our proposed method’s usefulness through experiments and through the measuring indexes of precision and recall.

Original languageEnglish
Title of host publicationRecent Advances on Soft Computing and Data Mining - Proceedings of the 3rd International Conference on Soft Computing and Data Mining SCDM 2018
EditorsJemal H. Abawajy, Rozaida Ghazali, Mustafa Mat Deris, Nazri Mohd Nawi
PublisherSpringer Verlag
Pages429-435
Number of pages7
ISBN (Print)9783319725499
DOIs
Publication statusPublished - Jan 1 2018
Event3rd International Conference on Soft Computing and Data Mining, SCDM 2018 - Johor, Malaysia
Duration: Feb 6 2018Feb 8 2018

Publication series

NameAdvances in Intelligent Systems and Computing
Volume700
ISSN (Print)2194-5357

Other

Other3rd International Conference on Soft Computing and Data Mining, SCDM 2018
CountryMalaysia
CityJohor
Period2/6/182/8/18

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science(all)

Cite this

Nakatoh, T., & Minami, T. (2018). Reducing computational effort for plagiarism detection with approximate string matching. In J. H. Abawajy, R. Ghazali, M. M. Deris, & N. M. Nawi (Eds.), Recent Advances on Soft Computing and Data Mining - Proceedings of the 3rd International Conference on Soft Computing and Data Mining SCDM 2018 (pp. 429-435). (Advances in Intelligent Systems and Computing; Vol. 700). Springer Verlag. https://doi.org/10.1007/978-3-319-72550-5_41