Titlebook: Data Mining and Big Data; Second International Ying Tan,Hideyuki Takagi,Yuhui Shi Conference proceedings 2017 Springer International Publis

非秘密

高深莫測(cè)

Peter D. Smallwood,Todd R. Lookingbill expected output for this proposed work is a SNS-integrated Virtual Learning Environment (VLE) named . which provides formal and informal learning for any subject domain area. User testing is conducted and both informal and formal data are stored and elicited from . to investigate the impact of this data combination for more insightful LA results.

Clumsy

Does Student’s Diligence to Study Relate to His/Her Academic Performance?rn effectively. Among various kinds of diligence, we take two kinds of them into consideration; the length of answer text to a questionnaire, and the amount of submitted homework assignments. We investigate how these kinds of diligence of students relate each other, and how they relate to the examination score.

Additive

Incremental Adaptive Time Series Prediction for Power Demand Forecasting only the most recent data to adapt the model (in contrast to all historical data) and adapting the model only when the need for it is detected (in contrast to creating a whole new model every day) enables the method to handle stream data. The proposed model shows promising results.

Redundant

Food Sales Prediction with Meteorological Data — A Case Study of a Japanese Chain Supermarketwhich are used to learn how sales changes with the weathers from a large amount of history data. We showed that our method gained initial success in predicting sales of some weather-sensitive products such as drinks. Particularly, our method outperforms traditional machine learning methods by 19.3%.

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