A fast algorithm for plagiarism detection in large-scale data

This paper proposes a fast plagiarism detection algorithm in large-scale data. Plagiarisms of superficial descriptions, such as "copy and paste", can be detected using a simple document similarity based on string matching. The algorithm reduces the effort for computing the document similarity by approximating the similarity. The effects of the approximation on the processing time and accuracy are evaluated by conducting experiments with a data set generated from practical scholarly documents. The experimental results show that the algorithm based on the approximated similarity can reduce the processing time of the straightforward algorithm based on the exact similarity to less than one-Third in exchange for a slight decrease of the accuracy.