Product-Impression Analysis Using Fuzzy C4.5 Decision Tree

Masataka Tokumaru; Noriaki Muranaka

doi:10.20965/jaciii.2009.p0731

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JACIII Vol.13 No.6 pp. 731-737

doi: 10.20965/jaciii.2009.p0731

(2009)

Paper:

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Product-Impression Analysis Using Fuzzy C4.5 Decision Tree

Masataka Tokumaru and Noriaki Muranaka

Faculty of Engineering Science, Kansai University, Osaka, Japan

Received:

December 10, 2008

Accepted:

May 7, 2009

Published:

November 20, 2009

Keywords:

fuzzy decision tree, impression analysis, usability, kansei information

Abstract

Product design requires careful consideration of many factors. However, it is difficult for many developers to design a product that will satisfy everyone, because they do not know the answers to questions such as “What causes large number of people to like a specific product?” “What fascinates them about the product?” or “Which part or element of the design makes it attractive?” In general, people like “usable” products, but it is difficult to know what makes certain products easy to use. This paper proposes a method for investigating “ease-of-use” by means of a fuzzy decision tree. It builds a decision tree based on the results of a questionnaire, which determines user's product impressions, and helps to uncover the major factors that affect the product's “ease-of-use.” In this paper, we analyzed golf clubs and investigated the factors that determine their particular ease-of-hitting, which is concerned with the ability to hit golf balls easily. As a result, we obtained some reliable rules affecting user impressions using the fuzzy decision tree built from the questionnaire results.

Cite this article as:

M. Tokumaru and N. Muranaka, “Product-Impression Analysis Using Fuzzy C4.5 Decision Tree,” J. Adv. Comput. Intell. Intell. Inform., Vol.13 No.6, pp. 731-737, 2009.

Data files:

References

[1] Y. Uchida, T. Yoshikawa, T. Furuhashi, E. Hirao, and H. Iguchi, “Evaluation of Products by Analysis of User-Review using HK Graph,” Proc. of Joint 4th Int. Conf. on Soft Computing and Intelligent Systems and 9th Int. Symposium on Advanced Intelligent Systems (SCIS & ISIS 2008), pp. 376-379, 2008.
[2] P. J. Tsai and S. Nagasawa, “Applied Research on the Product Planning of Cosmetics for Men,” Kansei Engineering Int., Vol.3, No.4, pp. 15-24, 2002.
[3] K. B. Lee and R. R. Grice, “Developing a New Usability Testing Method for Mobile Devices,” Proc. of 2004 Int. Professional Communication Conf., pp. 115-127, 2004.
[4] T. Tsuchiya and Y. Matsubara, “Non-Liner Data Analysis on Kansei Engineering and Design Evaluation by Genetic Algorithm,” Kansei Engineering Int., Vol.6, No.4, pp. 55-62, 2006.
[5] J. Lu, X. Deng, P. Vroman, X. Zeng, J. Ma, and G. Zhang, “Fuzzy Multi-Criteria Group Decision Support System for Nonwoven Based Cosmetic Product Development Evaluation,” Proc. of 2008 IEEE Int. Conf. on Fuzzy Systems, pp. 1700-1707, 2008.
[6] K. Hori, H. Satoh, M. Umano, and Y. Uno, “Fuzzy C4.5 for generating fuzzy decision trees and its improvement,” Proc. of the 4th Asian Fuzzy Systems Symposium, pp. 881-884, 2000.
[7] C. E. Osgood, G. Suci, and P. Tannenbaum, “The Measurment of Meaning,” University of Illinois Press, 1957.
[8] J. R. Quinlan, “C4.5: Program for Machine Learning,” Morgan Kaufmann Publishers, 1993.
[9] M. Tokumaru, N. Muranaka, and S. Imanishi, “Fuzzy Decision Tree Analysis to Investigate What Has an Influence on Ease-of-Use and Preference with Products,” The 8th World Multi-Conf. on Systemics, Cybernetics and Informatics, Vol.1 Information Systems, Technologies and Applications, pp. 433-438, 2004.
[10] J. R. Quinlan, “Induction of Decision Trees,” Machine Learning, Vol.1, pp. 81-106, 1986.
[11] P. A. Devijver and J. Kittler, “Pattern Recognition: A Statistical approach,” Prentice-Hall Int., 1982.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] Y. Uchida, T. Yoshikawa, T. Furuhashi, E. Hirao, and H. Iguchi, “Evaluation of Products by Analysis of User-Review using HK Graph,” Proc. of Joint 4th Int. Conf. on Soft Computing and Intelligent Systems and 9th Int. Symposium on Advanced Intelligent Systems (SCIS & ISIS 2008), pp. 376-379, 2008.

[2] [2] P. J. Tsai and S. Nagasawa, “Applied Research on the Product Planning of Cosmetics for Men,” Kansei Engineering Int., Vol.3, No.4, pp. 15-24, 2002.

[3] [3] K. B. Lee and R. R. Grice, “Developing a New Usability Testing Method for Mobile Devices,” Proc. of 2004 Int. Professional Communication Conf., pp. 115-127, 2004.

[4] [4] T. Tsuchiya and Y. Matsubara, “Non-Liner Data Analysis on Kansei Engineering and Design Evaluation by Genetic Algorithm,” Kansei Engineering Int., Vol.6, No.4, pp. 55-62, 2006.

[5] [5] J. Lu, X. Deng, P. Vroman, X. Zeng, J. Ma, and G. Zhang, “Fuzzy Multi-Criteria Group Decision Support System for Nonwoven Based Cosmetic Product Development Evaluation,” Proc. of 2008 IEEE Int. Conf. on Fuzzy Systems, pp. 1700-1707, 2008.

[6] [6] K. Hori, H. Satoh, M. Umano, and Y. Uno, “Fuzzy C4.5 for generating fuzzy decision trees and its improvement,” Proc. of the 4th Asian Fuzzy Systems Symposium, pp. 881-884, 2000.

[7] [7] C. E. Osgood, G. Suci, and P. Tannenbaum, “The Measurment of Meaning,” University of Illinois Press, 1957.

[8] [8] J. R. Quinlan, “C4.5: Program for Machine Learning,” Morgan Kaufmann Publishers, 1993.

[9] [9] M. Tokumaru, N. Muranaka, and S. Imanishi, “Fuzzy Decision Tree Analysis to Investigate What Has an Influence on Ease-of-Use and Preference with Products,” The 8th World Multi-Conf. on Systemics, Cybernetics and Informatics, Vol.1 Information Systems, Technologies and Applications, pp. 433-438, 2004.

[10] [10] J. R. Quinlan, “Induction of Decision Trees,” Machine Learning, Vol.1, pp. 81-106, 1986.

[11] [11] P. A. Devijver and J. Kittler, “Pattern Recognition: A Statistical approach,” Prentice-Hall Int., 1982.