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Open Access

Knowledge Map Mining of Financial Data

Wenhui ShouWenhui Fan( )Boyuan LiuYuyang Lai
Department of Automation, Tsinghua University, Beijing 100084, China
SOYOTEC Technologies Co., Ltd., Beijing 100081, China.
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Abstract

The Knowledge Map (KM) concept, which was derived from the Fuzzy Cognitive Map (FCM), is used to describe and manage knowledge. KM provides insight into the interdependencies and uncertainties contained in the system. This paper uses a model-free method to mine KMs in historical data to analyze component stock corporations of the Shanghai Stock 50 index. The analyses use static and time-domain analyses. The results indicate that a knowledge map is useful for representing knowledge and for monitoring the health of companies. Furthermore, sudden changes of the key features of the KMs should be taken seriously by policymakers as an alarm of a crisis.

References

[1]
B. Kosko, Fuzzy cognitive maps, International Journal of Man-Machine Studies, vol. 24, no. 3, pp. 65-75, 1986.
[2]
J. Aguilar, A survey about fuzzy cognitive maps papers, International Journal of Computational Cognition, vol. 3, no. 2, pp. 27-33, 2005.
[3]
L. Rodriguez-Repiso, R. Setchi, and J. L. Salmeron, Modelling IT projects success with fuzzy cognitive maps, Expert Systems with Applications, vol. 32, no. 2, pp. 543-559, 2007.
[4]
Z. Peng, B. Yang, C. Liu, Z. Tang, and J. Yang, Research on one fuzzy cognitive map classifier, (in Chinese), Application Research of Computers, vol. 26, no. 5, pp. 1757-1759, 2009.
[5]
T. Hong and I. Han, Knowledge-based data mining of news information on the Internet using cognitive maps and neural networks, Expert Systems with Applications, vol. 23, no. 1, pp. 1-8, 2002.
[6]
E. I. Papageorgiou, Learning algorithms for fuzzy cognitive mapsła review study, IEEE Trans on Systems, Man and Cybernetics, vol. 42, no. 2, pp. 150-163, 2012.
[7]
J. A. Dickerson and B. Kosko, Virtual worlds as fuzzy cognitive maps, Presence, vol. 3, no. 2, pp. 173-189, 1994.
[8]
M. Schneider, E. Shnaider, A. Kandel, and G. Chew, Constructing fuzzy cognitive maps, in Proc. 1995 IEEE International Conference on Fuzzy Systems, Yokohama, Japan, 1995, pp. 2281-2288.
[9]
K. E. Parsopoulos, E. I. Papageorgiou, P. P. Groumpos, and M. N. Vrahatis, A first study of fuzzy cognitive maps learning using particle swarm optimization, in Proc. 2003 Congress on Evolutionary Computation, 2003, pp. 1440-1447.
[10]
W. Stach, L. Kurgan, W. Pedrycz, and M. Reformat, Learning fuzzy cognitive maps with required precision using genetic algorithm approach, Electronics Letters, vol. 40, no. 24, pp. 1519-1520, 2004.
[11]
G. Allen and J. Marczyk, Tutorial on complexity management for decision-making, http://www.e-education.psu.edu/drupal6/files/sgam/Tutorial%20on%20Complexity%20Management%20for%20Decision-Making.pdf, 2012.
[12]
J. Marczyk, A New Theory of Risk And Rating, Trento: Editrice Uni Service, 2009.
[13]
D. V. Steward, The design structure matrix: A method for managing the design of complex systems, IEEE Transactions on Engineering Management, vol. EM-28, no. 3, pp.71-74, 1981.
[14]
S. Aumonier, Generalized correlation power analysis, in Proc. ECRYPT Workshop on Tools For Cryptanalysis, Krakw, Poland, 2007.
[15]
C. E. Shannon, A mathematical theory of communication, Bell System Technical Journal, vol. 27, pp.379-423, 1948.
[16]
B. Lent, A. Swami, and J. Widom, Clustering association rules, in Proc. 13th International Conference on Data Engineering, Birmingham, England, 1997, pp. 220-231.
Tsinghua Science and Technology
Pages 68-76
Cite this article:
Shou W, Fan W, Liu B, et al. Knowledge Map Mining of Financial Data. Tsinghua Science and Technology, 2013, 18(1): 68-76. https://doi.org/10.1109/TST.2013.6449410

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Received: 20 April 2012
Accepted: 23 November 2012
Published: 07 February 2013
© The author(s) 2013
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