Paper Info
Title
Text Mining for Plagiarism Detection: Multivariate Pattern Detection for Recognition of Text Similarities.
Abstract
The problem of plagiarism the recent years has been intensified by the availability of information in digital form and the accessibility of the electronic libraries through the Internet. As a result, plagiarism detection has been transformed into a big data analytics problem since the number of digital sources is extravagant and a new document needs to be compared with millions of other existing documents. In this paper, a text mining methodology is proposed that can detect all common patterns between a document and the documents in a reference database. The technique is based on a pattern detection algorithm and the corresponding data structure that enables the algorithm to detect all common patterns. The methodology has been applied in a well-defined dataset providing very promising results identifying difficult cases of plagiarism such as technical disguise.
Year
DOI
Venue
2018
10.5555/3382225.3382424
ASONAM '18: International Conference on Advances in Social Networks Analysis and Mining Barcelona Spain August, 2018
Keywords
Field
DocType
plagiarism detection, text mining, ARPaD, LERP-RSA
Data mining,Data structure,Text mining,Plagiarism detection,Computer science,Multivariate statistics,Reference database,Pattern detection,Big data,The Internet
Conference
ISBN
Citations 
PageRank 
978-1-5386-6051-5
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Konstantinos F. Xylogiannopoulos1187.74
Panagiotis Karampelas23415.16
Reda Alhajj31919205.67