Triangular Inequality based K-means Clustering Method for Online Learning Platform for College English on Web Technology

The teaching in online is widely becoming the part of learning procedure and have much well-estimated in developing countries. The traditional College English online education platforms have the limitations of less user satisfaction and huge response time. This research proposed Triangular Inequality based K-means Clustering Algorithm on web platform and analyzes the relevance application tests. By using the web, the software and hardware platforms are developed. The hardware platform included the modules of student, teacher, admin and database. The Triangular Inequality based K-means Clustering method is utilized for clustering learners’ information in web for determining their college English level and then collaborative filtering technique is integrated for recommending relevance course materials for learners for finishing the software design. The web platform application is analyzed with platform on small program and shows effective performance of 9.7 mean values. The outcomes represent that platform has benefits of effective function and security and less response time.