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General Information
    • ISSN: 2010-3700 (Online)
    • Abbreviated Title: Int. J. Mach. Learn. Comput.
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Scopus (since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
    • E-mail: ijmlc@ejournal.net
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.

IJMLC 2019 Vol.9(1): 67-74 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2019.9.1.767

Application of Kansei Engineering and Data Mining in Developing an Ingenious Product Co-design System

Kittipong Sakornsathien, Sukree Sinthupinyo, and Pongpun Anuntavoranich
Abstract—This research intends to be a part in developing an automatic system that shows the potential of designing a product form by co-designing with the user in order to make it suitable for each consumer by applying Kansei Engineering Technique. However, the operation of KE needs data collection from specific target group in order to devise a formula and interpret the results when applying to study the same target group, or else the accuracy will be reduced. In order to eliminate the drawbacks, Data Mining techniques will be applied with KE system in order to use as an ingenious product co-design system.
The style and preference of each user will be used as a categorizing factor clustering the database into groups with K-means technique, in this research. Each classifying cluster will use its own database in the system processing in order to gain a set of design elements precisely from the system. Decision Tree classification technique is selected for the study. For the model validation, we apply the cross-validation as an unbiased model performance evaluation. In order to build the KE system, the sentiments in this research are indicated by 5 pair-words constituting the system data. The result shows that, this method offers more accurate prediction of design elements comparing to the method which undivided users.

Index Terms—Data mining, machine learning, Kansei engineering, product development.

K. Sakornsathien is with the School of Technopreneurship and Innovation Management, Graduate School, Chulalongkorn University, 254 Phayathai Rd., Phatumwan Bangkok, 10330 Thailand (e-mail: kittipong.s@student.chula.ac.th).
S. Sinthupinyo is with the Department of Computer Engineering, Chulalongkorn University, 254 Phayathai Rd., Phatumwan Bangkok, 10330 Thailand (e-mail: sukree.s@chula.ac.th).
P. Anantavoranich is with the Department of Industrial Design, Faculty of Architecture, Chulalongkorn University254 Phayathai Rd., Phatumwan Bangkok, 10330 Thailand (e-mail: p.idchula@gmail.com).

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Cite: Kittipong Sakornsathien, Sukree Sinthupinyo, and Pongpun Anuntavoranich, "Application of Kansei Engineering and Data Mining in Developing an Ingenious Product Co-design System," International Journal of Machine Learning and Computing vol. 9, no. 1, pp. 67-74, 2019.

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