An Effective Compressive Sensing based N-gram Approach for plagiarism detection
Nosheena Khan, Chetan Agrawal, Himanshu Yadav · 2020
Plagiarism detection over the document is an important area of research, which directly deals with document and its content copying. Many algorithms for such document processing such as word matching, string matcher, similarity measure and Rabin-Karp etc. were proposed. Previous approaches are limited while dealing with data analysis work with single level of processing. Thus, they either work with document pre-processing execution or further plagiarism detection. In this paper, an advance novel approach which is compressive sensing based on N-gram (CS-RKP) is proposed. This algorithm used sampling module for data processing and further cost function for document redundancy detection, minimization of iteration and further finding similarity over the document. The result observation using computation time, similarity measure shows the efficiency of proposed algorithm.