(untitled) (International Journal of Engineering and Computer Science, 2016)
— Mining the needed data based on our application was the crucial activity in the computerized environment.For that mining techniques was introduced.This project used to extract the mobile apps.The Ranking fraud in the mobile App market refers to fraudulent or deceptive activities which have a purpose of bumping up the Apps in the popularity list. Indeed, it becomes more and more frequent for App developers to use shady means, such as inflating their Apps’ sales or posting phony App ratings, to commit ranking fraud. Here first propose to accurately locate the ranking fraud by mining the active periods, namely leading sessions, of mobile Apps. Furthermore, we investigate three types of evidences, i.e., ranking based evidences, rating based evidences and review based evidences, by modeling Apps’ ranking, rating and review behaviors through statistical mining based hypotheses tests. In addition, In this project an optimization based application used to integrate all the evidences for fraud detection based on EIRQ (efficient information retrieval for ranked query) algorithm. Finally, evaluate the proposed system with real-world App data collected from the iOS App Store for a long time period. Experiment was need to be done for validate the effectiveness of the proposed system, and show the scalability of the detection algorithm as well as some regularity of ranking fraud activities.