Title :
Intelligent feature extraction and knowledge mining by multivariate analyses
Author :
Chen, Yisong ; Cui, Hong
Author_Institution :
Key Lab. of Machine Perception, (Minist. of Educ.), Peking Univ., Beijing
fDate :
March 30 2009-April 2 2009
Abstract :
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.
Keywords :
data analysis; data mining; data reduction; data visualisation; decision making; educational administrative data processing; feature extraction; pattern classification; statistical analysis; support vector machines; data analysis; data dimensionality reduction; data interpretation; decision-making; factor analysis; filtered data classification; intelligent data analysis; intelligent feature extraction; knowledge mining; multidimensional scaling; multivariate analyses; school grading policy; subsequent data visualization; support vector machine; Analytical models; Data analysis; Data visualization; Decision making; Educational institutions; Feature extraction; Machine intelligence; Multidimensional systems; Support vector machine classification; Support vector machines;
Conference_Titel :
Computational Intelligence and Data Mining, 2009. CIDM '09. IEEE Symposium on
Conference_Location :
Nashville, TN
Print_ISBN :
978-1-4244-2765-9
DOI :
10.1109/CIDM.2009.4938626
