Kernel principal component clustering methodology for stock categorization

Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice

Volumn 29, Issue 12, 2009, Pages 1-8

  Yu, Le An ^a,b   Wang, Shou Yang ^a,b  

^a CHINESE ACADEMY OF SCIENCES   (China)

^b Financial Engineering and Financial Management Research Center of Hunan Province   (China)

Author keywords

K means clustering; Kernel principal component analysis; Kernel principal component clustering; Stock categorization

Indexed keywords

CHINESE STOCK MARKET; CLUSTERING ANALYSIS; CLUSTERING APPROACH; CLUSTERING RESULTS; DATA DIMENSIONS; EFFICIENT PORTFOLIO; EMPIRICAL ANALYSIS; FINANCIAL INFORMATION; HIGH QUALITY; K-MEANS CLUSTERING; K-MEANS CLUSTERING METHOD; KERNEL PRINCIPAL COMPONENT; KERNEL PRINCIPAL COMPONENT ANALYSIS; LISTED COMPANIES; REDUCED DATA; SAMPLE DATA; TRADITIONAL CLUSTERING;

CLUSTER ANALYSIS; CLUSTERING ALGORITHMS; WATER SUPPLY SYSTEMS;

PRINCIPAL COMPONENT ANALYSIS;

EID: 74249107959     PISSN: 10006788     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (9)

1. Min D. The application of art in the classification of security[J]. Taxation and Economy, 2006(3): 52-54.
2. Zhang Y C, Zhang Z Q, Huang Z. The application of support vector machine in section of high-quality security[J]. Statistics and Decision, 2008(4): 163-165.
3. Li Y F, Hui X F. The classification model for stock investment value based on SVM[J]. China Soft Science, 2008(1): 135-140.
4. Yang M Z, Zhao C G, Gu Z Y. Research of the stock classification based on the PCA and rough set[J]. Science Technology and Engineering, 2009(4): 1092-1096.
5. Di M M, Sun D S. The application of clustering analysis and support vector machines in stock market[J]. 2009, 19(6): 229-231.
6. Scholkopf B, Smola A, Muller K R. Nonlinear component analysis as a kernel eigenvalue problem[J]. Neural Computation, 1998, 10(5): 1299-1319.
7. Vapnik V. The Nature of Statistical Learning Theory[M]. New York: Springer, 1995.
8. Ding C, He X. K-means clustering via principal component analysis[C]//Proceedings of the 2004 International Conference on Machine Learning, 2004: 225-232.
9. Jollifie I T. Principal Component Analysis[M]. Springer Series in Statistics, New York: Springer, 2002.