Rough set-based interrelationship mining - A new approch for Kansei data analysis -

2015 Fiscal Year Final Research Report

• Project/Area Number: 25330315
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Research Field: Kansei informatics
• Research Institution: Muroran Institute of Technology
• Principal Investigator: KUDO Yasuo 室蘭工業大学, 工学(系)研究科(研究院), 准教授 (90360966)
• Co-Investigator(Kenkyū-buntansha): MURAI Tetsuya 北海道大学, 情報科学研究科, 准教授 (90201805)
• Co-Investigator(Renkei-kenkyūsha): OKADA Yoshifumi 室蘭工業大学, 工学研究科, 准教授 (00443177)
• Project Period (FY): 2013-04-01 – 2016-03-31
• Keywords: 関係性マイニング / ラフ集合 / 感性情報処理

Outline of Final Research Achievements

In this study, we proposed a new approach for Kansei data analysis based on rough set-based interrelationship mining. We firstly proposed the concept of the interrelationship mining that enables us to extract characteristics based on comparison of attribute values between different attributes. In the interrelationship mining, the relationship between attribute values are explicitly described by newly introduced attributes called interrelated attributes. Addition of the interrelated attributes for the interrelationship mining causes increase of the number of attributes. We then constructed rough set-based attribute reduction systems for datasets with many objects and attributes. We also discussed to apply the rough set-based interrelationship mining to Kansei data analysis. Consequently, our study contributed to provide a new approach for rough set-based Kansei data analysis.

Free Research Field

感性情報学