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Intelligent feature extraction and knowledge mining by multivariate analyses

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2 Author(s)
Yisong Chen ; Key Lab. of Machine Perception, (Minist. of Educ.), Peking Univ., Beijing ; Hong Cui

A new knowledge mining framework based on multivariate analyses is proposed to discover and simulate the school grading policy. The framework comprises three major steps. Firstly, factor analysis is adopted to separate the scores of several different subjects into grading-related ones and grading-unrelated ones. Secondly, multidimensional scaling is employed for dimensionality reduction to facilitate subsequent data visualization and interpretation. Finally, a support vector machine is trained to classify the filtered data into different grades. This work provides an attractive framework for intelligent data analysis and decision-making. It also exhibits the advantages of high classification accuracy and supports intuitive data interpretation.

Published in:

Computational Intelligence and Data Mining, 2009. CIDM '09. IEEE Symposium on

Date of Conference:

March 30 2009-April 2 2009