Hybrid Association Mining and Refinement for Affective Mapping in Emotional Design

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