International Association of Engineers
churn is defined as the loss of a user in an Online Social Network (OSN). Detecting and analyzing user churn at an early stage helps to provide timely delivery of retention solutions (e.g., interventions, customized services, and better user interfaces) that are useful for preventing users from churning. In this paper, the authors develop a prediction model based on a clustering scheme to analyze the potential churn of users. In the experiment, they test their approach on a real-name OSN which contains data from 77,448 users. A set of 24 attributes is extracted from the data.