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

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.

*J. Adv. Comput. Intell. Intell. Inform.*, Vol.18, No.6, pp. 953-961, 2014.

- [1] Z. Pawlak, “Rough Sets: Theoretical Aspects of Reasoning About Data,” Kluwer Academic Publishers, 1991.
- [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] M. Kryszkiewicz, “Rough set approach to incomplete information systems,” Information Sciences, Vol.112, No.1-4, pp. 39-49, 1998.
- [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] E. Orłowska and Z. Pawlak, “Representation of nondeterministic information,” Theoretical Computer Science, Vol.29, No.1-2, pp. 27-39, 1984.
- [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] 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] H. Sakai, “RNIA software logs, 2011,”

http://www.mns.kyutech.ac.jp/∼sakai/RNIA [Accessed January 2011] - [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] 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] 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] 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] 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] M. Wu and H. Sakai, “
*getRNIA*web software,” 2013.

http://getrnia.org/ [Accessed June 2013] - [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] 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] R. Agrawal and R. Srikant, “Fast algorithms for mining association rules in large databases,” Proc. of VLDB’94, pp. 487-499, 1994.
- [18] A. Ceglar and J. F. Roddick, “Association mining,” ACM Computing Survey, Vol.38, No.2, 2006.
- [19] Kripke semantics in Wikipedia

http://en.wikipedia.org/wiki/Kripke_semantics [Accessed January 2011] - [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]