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Clustering Analysis of Stocks of CSI 300 Index Based on Manifold Learning
International School of Software, Wuhan University, Wuhan, China
International School of Software, Wuhan University, Wuhan, China
1International School of Software, Wuhan University, Wuhan, China; 2Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China.
- 1 International School of Software, Wuhan University, Wuhan, China
- 2 International School of Software, Wuhan University, Wuhan, China
- 3 1International School of Software, Wuhan University, Wuhan, China; 2Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China.
Journal of Intelligent Learning Systems and Applications·Volume 04 (2012)·Pages 120–126·Published 23 May 2012·DOI10.4236/jilsa.2012.42011
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Abstract
As an effective way in finding the underlying parameters of a high-dimension space, manifold learning is popular in nonlinear dimensionality reduction which makes high-dimensional data easily to be observed and analyzed. In this paper, Isomap, one of the most famous manifold learning algorithms, is applied to process closing prices of stocks of CSI 300 index from September 2009 to October 2011. Results indicate that Isomap algorithm not only reduces dimensionality of stock data successfully, but also classifies most stocks according to their trends efficiently.
KeywordsManifold LearningIsomapNonlinear Dimensionality ReductionStock Clustering
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