A Probabilistic Rough Set Approach to Rule Discovery

2011 ◽  
pp. 55-65 ◽  
Author(s):  
Hossam Abd Elmaksoud Mohamed
Keyword(s):  
Rough Set ◽  
Rule Discovery

Hybrid Association Mining and Refinement for Affective Mapping in Emotional Design

2010 ◽  
Vol 10 (3) ◽  
Author(s):  
Feng Zhou ◽  
Jianxin Roger Jiao ◽  
Dirk Schaefer ◽  
Songlin Chen
Keyword(s):  
Interior Design ◽  
Rough Set ◽  
Association Mining ◽  
Rule Discovery ◽  
Emotional Design ◽  
Design Elements ◽  
Affective Quality ◽  
Mapping Process ◽  
Rule Refinement

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.

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