This research is concerned with the detection of similar academic papers. Given a tested paper from a given corpus of 10,099 peer-reviewed scientific papers, a two-stage process was activated. During the first stage, most of the papers were filtered out using a fast filter method. In the second stage, in order to detect similar papers we applied 23 heuristic variants derived from 3 novel prototype methods using various parameter settings. The three novel prototype methods are: CT-TR – Constant Number of randomized T fingerprints, compared to each one-third of R (first/middle/last) fingerprints, CT-AR: Constant Number of randomized T fingerprints, compared to all R fingerprints, and CDT-AR: Constant Number of divided randomized T fingerprints compared, to all R fingerprints. Results achieved by the new methods are superior to those of previous heuristic methods, which were approximations of the “Full Fingerprint” (FF) method, currently considered the best heuristic method. The order of this new methods' run-time, Θ(n), is far more efficient than the order of the FF method run-time, Θ(n2) (after removing short documents from the corpus).
|Number of pages||17|
|Journal||Information Processing and Management|
|State||Published - 1 Jan 2017|
Bibliographical noteFunding Information:
This work was partially funded by an internal research grant from Jerusalem College of Technology , Lev Academic Center. The authors thank Alex Klein, Shlomo Engelberg, and two anonymous reviewers for their help and fruitful comments.
© 2016 Elsevier Ltd
- Heuristic methods
- Plagiarism detection
- Similar peer-reviewed scientific papers