With the evolution of new technology especially in the domain of e-commerce and online banking, the payment by credit card has seen a significant increase. The credit card has become the most used tool for online shopping. This high rate in use brings about fraud and a considerable damage. It is very important to stop fraudulent transactions because they cause huge financial losses over time. The detection of fraudulent transactions is an important application in anomaly detection. There are different approaches to detecting anomalies namely SVM, logistic regression, decision tree and so on. However, they remain limited since they are supervised algorithms that require to be trained by labels in order to know whether the transactions are fraudulent or not. The goal of this paper is to have a credit card fraud detection system which is able to detect the highest number of new transactions in real time with high accuracy. We will also compare, in this paper, different unsupervised techniques for credit card fraud detection namely LOF, one class SVM, Kmeans and Isolation Forest so as to single out the best approach.

The issue of land use (LU) and land cover change (LCC) has become crucial around the world in recent years, not only for researchers, but also for urban planners and environmentalists who advocate sustainable land use in the future. In Morocco, this phenomenon affects large areas and is all the more pronounced because the climate is arid with cycles of increasing drought and soils are poor and highly vulnerable to erosion. In addition, the precarious living conditions of rural populations pushes them to over exploit natural resources to meet their growing needs, which further amplifies environmental degradation. In this LU/LCC monitoring context, this paper aims on one hand at giving a clear survey of classical methods and techniques used to monitor LU/LCC, on other hand the authors propose a new architecture whose objective is to integer data mining techniques to the LU/LCC monitoring in order to automatically and efficiently improve the monitoring, control and asset management in LU/LCC..

France has the second largest European railway network, with a total of 29,901 kilometers of railway. However, the travel experience of passengers is frequently marked by delays, late arrival of trains at stations, causing inconvenience. The purpose of this paper is to present a new approach for visual prediction of train delays. Our approach is driven by predictive analysis and interactive visualization. The study has benefitted from access to open data SNCF including information about train delays , train number , station , departure and arrival time .Based on this data we develop a new workflow for predictive analysis including visualization in all steps from data pre-processing to deployment .