Agents Oriented Genetic-K-Means (AOGK) System for Plagiarism Detection

In the last decade, the plagiarism phenomenon has widely spread and become a topical problem in the modern scientific world, caused by the wide availability of electronic documents online and offline. This work will be devoted to describe a new plagiarism detection system named AOGK « Agents Oriented Genetic-K-means » based on a multi-agents architecture composed of three modules: text parsing to transform documents into vectors; Learning module using genetic algorithms to build a prediction model; Test module using k-means for the final classification of suspicious document; To evaluate their system the authors have used a range of reference metrics (precision, recall, f-measure and entropy) and the benchmark PAN 09. They have compared the results obtained with the performance of other systems found in literature; the authors' aim is the preservation of copyright.