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Rough Set Model Based Knowledge Acquisition of Market Movements from Economic Data

Chapter
Part of the Studies in Big Data book series (SBD, volume 8)

Abstract

The concept and method of rough sets were proposed by Z. Pawlak in 1982. This method enables us to mine knowledge granules as decision rules from a database, a web base, a set and so on. The obtained decision rules can be applicable for data analysis as well as used to reason, estimate, evaluate, or forecast an unknown object. The objective of this paper is to apply the rough set method to time series data for mining knowledge granules, and especially to mine knowledge granules from the data set of tick-wise price fluctuations.

Keywords

Rough set model Knowledge acquisition Market movement Economic data Knowledge granule Decision rule Data analysis Tick-wise price 

References

  1. 1.
    Matsumoto, Y., Watada, J.: Improvement of chaotic short-term forecasting on fuzzy reasoning and tuning on genetic algorithm. Jpn. Soc. J. Fuzzy Theory Intell. Inf. 16(1), 44–52 (2004)Google Scholar
  2. 2.
    Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11(5), 341–356 (1982)CrossRefMATHMathSciNetGoogle Scholar
  3. 3.
    Pawlak, Z.: Rough Sets—Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers (1991)Google Scholar
  4. 4.
    Tan, S., Cheng, X., Xu, H.: An efficient global optimization approach for rough set based dimensionality reduction. Int. J. Innov. Comput., Info. Control 3(3), 725–736 (2007)Google Scholar
  5. 5.
    Goh, C., Law, R.: Incorporation the rough sets theory. Chemometr. Intell. Lab. Syst. 47(1), 1–16 (2003)Google Scholar
  6. 6.
    Azibi, R., Vanderpooten, D.: Construction of rule-based assignment models. Eur. J. Oper. Res. 138(2), 274–293 (2002)CrossRefMATHMathSciNetGoogle Scholar
  7. 7.
    Beynon, M.J., Peel, M.J.: Variable precision rough set theory and data discrimination: an application to corporate failure prediction. Omega 29(6), 561–576 (2001)CrossRefGoogle Scholar
  8. 8.
    Li, R., Wang, Z.O.: Mining classification rules using rough set and neural networks. Eur. J. Oper. Res. 157(2), 439–448 (2004)CrossRefMATHGoogle Scholar
  9. 9.
    Quafafou, M.: α-RST: a generalization of rough set theory. Inf. Sci. 124(4), 301–316 (2000)CrossRefMATHMathSciNetGoogle Scholar
  10. 10.
    Greco, S., Matarazzo, B., Slowinski, R.: Rough sets theory for multi-criteria decision analysis. Eur. J. Oper. Res. 129(1), 1–47 (2001)CrossRefMATHMathSciNetGoogle Scholar
  11. 11.
    Jhieh, Y., Tzeng, G., Wang, F.: Rough set theory in analyzing the attributes of combination values for insurance market. Expert Syst. Appl. 32(1), 56–64 (2007)CrossRefGoogle Scholar
  12. 12.
    Harada, T., Tanaka, R.: Analysis of Specifications for web screen-design using rough sets. J. Adv. Comput. Intell. Intell. Info. 10(5), 688–694 (2006)Google Scholar
  13. 13.
    Kim, D., Bang, S.Y.: IRIS data classification using tolerant rough sets. J. Adv. Comput. Intell. Intell. Info. 4(5) (2000)Google Scholar
  14. 14.
    Walczak, B., Massart, D.L.: Rough set theory. Chemom. Intell. Lab. 47(1), 1–16 (1999)CrossRefGoogle Scholar
  15. 15.
    Predki, B., Slowinski, R., Stefanowski, R., Wilk, S.z.: ROSE-software implementation of the rough set theory. In: Polkowski, L., Skowron, A. (eds.) Rough Set and Current Trends in Computing. Lecture Notes in Artificial Intelligence, Springer, Berlin, pp. 605–608, (1998)Google Scholar
  16. 16.
    Predki, B., Wilk, S.z.: Rough set based data exploration using ROSE system. In: Ras, Z.W, Skowron, A. (eds.) Foundations of Intelligent Systems. Lecture Notes in Artificial Intelligence, Poland, Warsaw: Springer, pp. 172–180, (1999)Google Scholar
  17. 17.
    Pawlak, Z.: Rough classification. Int. J. Hum.-Comput. Stud. 51(15), 369–383 (1999)CrossRefGoogle Scholar
  18. 18.
    Gronhaug, K., Gilly, M.C.: A transaction cost approach to consumer dissatisfaction and complaint action. J. Econ. Psychol. 12(1), 165–183 (1991)CrossRefGoogle Scholar
  19. 19.
    Lin, C.,Watada, J., Tzeng, G.: Rough sets theory and its application to management engineering. In: Proceedings, international symposium of management engineering, Kitakyushu, Japan, pp. 170–176, (2008)Google Scholar
  20. 20.
    Tanaka, H Tsumoto, S.: Rough sets and expert system, Math. Sci., pp. 76–83 (1994)Google Scholar
  21. 21.
    Mori, N., Tanaka, H., Inoue, K.: Rough sets and Kansei: knowledge acquisition and reasoning from Kansei data, (2004)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Shimonoseki City UniversityShimonosekiJapan
  2. 2.Waseda UniversityKitakyushuJapan

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