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Optimization Model for Automatically Change of Phone Learning

Qiufen Yang, Canjun Li

Abstract


In recent years, with the development of mobile communications, the mobile learning which is a new learning model, as the carrier of important teaching in modern distance education, is gradually becoming a boom prevalent in the campus, the companies, and attracts more and more educators from all fields. For this problem, the mobile learning model based on the phone is studied in this article, and the optimized incremental fuzzy clustering algorithm is proposed based on the incremental improved policy, through starting from the key rules in the smart phone situation pattern learning model, the improved model of the associated clustering optimized model is proposed, in addition, the model is strengthened to proposed a incremental fuzzy clustering algorithm -EIFCA combining the entropy measure and the expecting entropy. Simulation results show that the user feedback mechanism based on EIFCA can significantly improve accuracy of the situation model rules.

Keywords


incremental improvement, phone learning, incremental fuzzy clustering, Strategy Optimization.

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