Hybrid Association Mining and Refinement for Affective Mapping in Emotional Design

Feng Zhao, Jianxin Roger Jiao, Dirk Schaefer, Songlin Chen

Research output: Contribution to journalArticlepeer-review

24 Citations (SciVal)


Emotional design entails a bidirectional affective mapping process between affective
needs in the customer domain and design elements in the designer domain. To leverage
both affective and engineering concerns, this paper proposes a hybrid association mining
and refinement (AMR) system to support affective mapping decisions. Rough set and K
optimal rule discovery techniques are applied to identify hidden relations underlying
forward affective mapping. A rule refinement measure is formulated in terms of affective
quality. Ordinal logistic regression (OLR) is derived to model backward affective mapping.
Based on conjoint analysis, a weighted OLR model is developed as a benchmark of
the initial OLR model for backward refinement. A case study of truck cab interior design
is presented to demonstrate the feasibility and potential of the hybrid AMR system for
decision support to forward and backward affective mapping
Original languageEnglish
Pages (from-to)031010-1 - 031010-9
Number of pages9
JournalJournal of Computing and Information Science in Engineering
Issue number3
Early online date3 Sept 2010
Publication statusPublished - 2010


  • Engineering Design


Dive into the research topics of 'Hybrid Association Mining and Refinement for Affective Mapping in Emotional Design'. Together they form a unique fingerprint.

Cite this