single-jc.php

JACIII Vol.18 No.6 pp. 953-961
doi: 10.20965/jaciii.2014.p0953
(2014)

Paper:

Application of Rough Set-Based Information Analysis to Questionnaire Data

Naoto Yamaguchi*, Mao Wu*, Michinori Nakata**,
and Hiroshi Sakai*

*Integrated System Engineering, Graduate School of Engineering, Kyushu Institute of Technology, Tobata, Kitakyushu 804-8550, Japan
**Faculty of Management and Information Science, Josai International University, Gumyo, Togane, Chiba 283-8555, Japan

Received:
December 27, 2013
Accepted:
May 14, 2014
Online released:
November 20, 2014
Published:
November 20, 2014
Keywords:
rough sets, table data analysis, missing values, questionnaire, question-answering
Abstract

This article reports an application of Rough Nondeterministic Information Analysis (RNIA) to two data sets. One is the Mushroom data set in the UCI machine leaning repository, and the other is a student questionnaire data set. Even though these data sets include many missing values, we obtained some interesting rules by using our getRNIA software tool. This software is powered by the NIS-Apriori algorithm, and we apply rule generation and question-answering functionalities to data sets with nondeterministic values.

References
  1. [1] Z. Pawlak, “Rough Sets: Theoretical Aspects of Reasoning About Data,” Kluwer Academic Publishers, 1991.
  2. [2] A. Skowron and C. Rauszer, “The discernibility matrices and functions in information systems,” Intelligent Decision Support – Handbook of Advances and Applications of the Rough Set Theory, pp. 331-362, Kluwer Academic Publishers, 1992.
  3. [3] M. Kryszkiewicz, “Rough set approach to incomplete information systems,” Information Sciences, Vol.112, No.1-4, pp. 39-49, 1998.
  4. [4] W. Lipski, “On semantic issues connected with incomplete information databases,” ACM Trans. on Database Systems, Vol.4, No.3, pp. 262-296, 1979.
  5. [5] E. Orłowska and Z. Pawlak, “Representation of nondeterministic information,” Theoretical Computer Science, Vol.29, No.1-2, pp. 27-39, 1984.
  6. [6] H. Sakai and A. Okuma, “Basic algorithms and tools for rough nondeterministic information analysis,” Trans. on Rough Sets, Vol.1, pp. 209-231, 2004.
  7. [7] H. Sakai, R. Ishibashi, K. Koba, and M. Nakata, “Rules and apriori algorithm in non-deterministic information systems,” Trans. on Rough Sets, Vol.9, pp. 328-350, 2008.
  8. [8] H. Sakai, “RNIA software logs, 2011,”
    http://www.mns.kyutech.ac.jp/∼sakai/RNIA [Accessed January 2011]
  9. [9] H. Sakai, M. Wu, and M. Nakata, “Association rule-based decision making in table data,” Int. J. of Reasoning-based Intelligent Systems, Vol.4, No.3, pp. 162-170, 2012.
  10. [10] H. Sakai, H. Okuma, M. Wu, and M. Nakata, “Rough nondeterministic information analysis for uncertain information,” The Handbook on Reasoning-Based Intelligent Systems, Chapter 4, pp. 81-118, World Scientific, 2013.
  11. [11] H. Sakai, M. Wu, and M. Nakata, “Division charts as granules and their merging algorithm for rule generation in nondeterministic data,” Int. J. of Intelligent Systems, Vol.28, No.9, pp. 865-882, 2013.
  12. [12] H. Sakai, M. Wu, and M. Nakata, “Apriori-based rule generation in incomplete information databases and non-deterministic information systems, Fundamenta Informaticae, Vol.130, No.3, pp. 343-376, 2014.
  13. [13] M.Wu, N. Yamaguchi, and H. Sakai, “Rough non-deterministic information analysis and its software tool: An overview,” Bulletin of the Kyushu Institute of Technology, Pure and Applied Mathematics, No.60, pp. 1-29, 2013.
  14. [14] M. Wu and H. Sakai, “getRNIA web software,” 2013.
    http://getrnia.org/ [Accessed June 2013]
  15. [15] M. Wu, M. Nakata, and H. Sakai, “An overview of the getRNIA system for non-deterministic data,” Procedia Computer Science, Vol.22, pp. 615-62, Elsevier, 2013.
  16. [16] N. Yamaguchi, M. Wu, M. Nakata, and H. Sakai, “Application of rough set-based information analysis to questionnaire data,” Proc. of FIM201, FIM2013-820-00224, 2013.
  17. [17] R. Agrawal and R. Srikant, “Fast algorithms for mining association rules in large databases,” Proc. of VLDB’94, pp. 487-499, 1994.
  18. [18] A. Ceglar and J. F. Roddick, “Association mining,” ACM Computing Survey, Vol.38, No.2, 2006.
  19. [19] Kripke semantics in Wikipedia
    http://en.wikipedia.org/wiki/Kripke_semantics [Accessed January 2011]
  20. [20] A. Frank and A. Asuncion, “UCI Machine Learning Repository,” Irvine, CA: University of California, School of Information and Computer Science, 2010.
    http://mlearn.ics.uci.edu/MLRepository.html [Accessed January 2011]

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Mar. 24, 2017