Integrating rough set theory and fuzzy association rule mining for product kansei knowledge analysis

抄録

Under the intensely competitive global economy, the new product development (NPD) has gradually changed from production-oriented to market-oriented. Therefore, it is an important factor to transform specific customers’ needs into shape appeal in products’ sale. The main purpose of this study is to analyze products’ Kansei knowledge by combining rough set theory (RST) and fuzzy association rule mining (FARM), thus providing decision support for NPD. The core Kansei needs that have a great impact on customers’ satisfaction is extracted in RST. FARM can identify the potential relationship between key Kansei attributes and product design attributes. The upright exercise bike is taken as the target case. It is found that the core customer needs of the exercise bike are “vitality”, “speed”, “luxury” and “stable”; while the “modern” which has no impact on emotional properties is deleted. Finally, the mapping relationship between the “vitality” Kansei image and the shape element is given priority. The result shows that the systematic design method combining RST and FARM has good predictive ability and significantly improves the efficiency of research and customers’ satisfaction.