Association Rule Mining for Affective Product Design

Association Rule Mining for Affective Product Design X. Yang1, D. Wu2, F. Zhou1, J. (Roger) Jiao1 1School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 2School of Management, Tianjin University, China ([email protected]; {PG23733595, zhou0099, mjiao}@ntu.edu.sg) between customer affect and perceptual design elements. Abstract - Affective product design implies a gradual The rest of the paper proceeds as follows. Section 2 shift of design focus from functional aspect to affec- presents the review of previous research. Section 3 de- tive/emotional aspect. It evolves as an interdisciplinary re- fines the problem context of affective product design. search topic between human factors, industrial design and Methodology of this study is described in section 4. In many other disciplines. This paper applies association rule section 5, a case study of Volvo truck cabs design is re- mining to reveal the mapping relations between customer affective needs and configuration of design elements. Gener- ported based on the proposed method. Conclusion is ic affective dimensions of truck cab were identified by clus- drawn in section 6. tering analysis. A goodness criterion was introduced to re- fine association rules. A case study of Volvo truck cab design Affective Product was conducted based on the proposed methods. customer ecosystem needs Acquire Analyze Fulfill Keywords - affective product design, association rule mining, conjoint analysis, clustering analysis Understand affective needs I. INTRODUCTION Today, satisfying customer needs has become a great Negotiate with designers concern of almost every company [1]. Among the whole spectrum of customer needs, functional and affective Customer Domain Designer Domain needs have been recognized to be of primary importance [2]. Traditional product design has focused on functional Fig.1. General process of affective design[5] aspect of user needs, making products easy to understand, easy to handle and so forth [3]. However, affective needs, which emphasize on customers’ emotional responses and II. BACKGROUND REVIEW aspirations, are arousing more and more attention in com- parison to the functional needs [2, 4]. In this context, Eliciting and measuring customer affect is known to emotional performance may be considered as an extra be problematic. This is because affect is classified as quality enhancing the value of physical products and em- “pleasure of the mind” and not accompanied by distinc- powering the company a competitive edge. tive facial expressions [6]. Due to this reason, physiologi- The challenge of affective product design is to cap- cal measures which are widely used in Human Factors are ture customers’ affective needs, to define the relationship not appropriate. Instead, subjective measures are adopted between the needs and product elements, and to explore by most of the researchers. Osgood, the affective properties that products intend to communi- Suci, & Tannenbaum [7] proposed semantic differential cate through their physical attributes. technique to analyze semantic structures and the affective Jiao et al. [5] proposed the general process of affec- meaning of things. Semantic differential technique has tive design (Fig.1). Two domains, customer domain and been successfully applied to find the semantic structure of designer domain, are the primary actors. Each has their designs, including the design of doors [8] , telephones[9], own interest and distinctive research issues are implied. office chair [10] and so forth. The semantic environ- Considering customer domain, for example, how to meas- ment description (SMB) was developed by Kuller [11] to ure customers’ affective responses and how to provide evaluate the impression of an architectural environment. them with products which satisfy their emotions? On the He validated 36 adjectives and grouped them into 7 fac- other hand, for the designers, how to facilitate the han- tors. Karlsson, Aronsson & Svensson [12] applied Kul- dling of affective needs information and assist them to ler’s method for evaluation of car interior design. The design better products? results succeeded in discriminating among four passenger This paper adopts a three-step approach to identify cars: BMW 318 (More complex and potent), Volvo S80 the affective dimensions of product design. After that, it (More original and Higher social status), Audi A6 (Less applies association rule mining to discover the mapping enclosed) and VW Bora (Greater affect). 978-1-4244-2630-0/08/$25.00 ©2008 IEEE 748 Proceedings of the 2008 IEEE IEEM Upon understanding customer affect dimensions, Association rule mining is a well established data mapping relations from customer affect to design ele- mining technique introduced by Agrawal & Srikant [18]. ments can be extracted by qualitative and quantitative The basic goal is to find interesting relations between methods. Qualitative methods are simple. For example, variables in large database. Two thresholds are required to focus group can be recruited to discuss the relationship. measure an association rule—support and confidence. The reliability of their results, however, is usually upon Support represents the frequency that items occur or co- further judgment of domain experts. Therefore, some occur in a transition record. Confidence represents the researchers employed statistical methods of multivariate degree to describe how strong an item subset X implies analysis, including multiple linear regression analysis another item subset Y. For the two domains in affective [13], general linear model [14] and so on. These methods design, a general association rule can be formulated as provide useful results but it has some theoretical limita- follows: tion, for customer affect do not always have linear charac- teristics. To overcome this limitation, non-linear methods dd&&&"" d⇒ ee &&& e (1) were proposed, including the quantification theory type I, ij k ij k ∈≤<<≤ II, III, and IV [15], genetic algorithm [16], neural net- where dddijk,, D ,1 i j k n , and works [17] and others. ∈≤<<≤ eeeijk,, E ,1 i j k n based on the support and confidence thresholds: III. PROBLEM FORMULATION Count(& dij d &"" & d k⇒ e ij & e & &) e k s = As shown in Fig.1, affective needs are usually ex- Count() DB (2) pressed by a set of affective descriptors D = {di| Sd(& d &"" & d⇒ e & e & &) e i=1,…,n}. Due to the diversity of customers, it is impera- c = ij k ij k tive to conduct stratified sampling based on different Sd(&ij d &" &) d k market segments. Assuming there are several markets, S = Count(& d d &"" & d⇒ e & e & &) e {si| i=1,…,I}, and each contains relatively homogenous = ij k ij k th customers, for customers in the i market segment, their Count(& d d &" &) d ij k (3) affective needs can be described as a subset . Meanwhile various perceptual product design elements are extracted from the designers. They can be character- C. Rule Refinement ized by another set E = {ei| i=1,…, m}. Each design ele- ment (factor) might have several levels. For example, the One challenge of association rule mining is the thre- interior color (factor) of a truck cab can have three levels shold decision. Low level of support and confidence leads (blue, red, and grey). A possible combination of design to overwhelming amount of rules. On the contrary, high elements with appropriate levels can be configured into a thresholds may filter out useful rules. It is necessary to set desired product. the criterion for goodness evaluation. Jiao, Zhang, & He- lander [19] proposed that a customer’s affective satisfac- tion can be interpreted as the customer’s expected utility III. METHODOLOGY embodied in a combination of affective descriptors, while his perceived utility is achieved via different configura- A. Elicitation and Measurement of Customer Affect tion of design elements. Therefore, a goodness criterion is introduced as the difference between the expected and Helander & Tay [6] explored the possibility to use the achieved utilities, shown in (4). The expected affective same set of affective descriptors across different products utility is represented as and observed affective and concluded that there were no universal affective di- utility as . For each association rule, if the observed mensions for product design. Hence in this study, we fol- utilities are greater than the expected utilities, it is consi- low a three stop approach to elicit driver affect of truck dered as good rule and retained. Otherwise it is regarded cab. The first step is to collect a large amount of affective as poor rule and discarded. descriptors that are representatives of consumer’s feelings on a product. The list of descriptors can be generated from ΔU = (4) user interviews, related journals and magazines, and where ∑ u ε ε (5) words used by marketing and design personnel In the second step, the most relevant and appropriate words are ∑ u ε ε (6) selected. The number of selected words varies from several dozens to several hundred. The selected words result- stands for the vector of affective descriptors in the left ing from second step are clustered to identify the affective side of the rule i, and the vector of design elements in dimensions. the right side of the rule i. To compare the utility from two different areas, (7) is introduced to normalize the ex- B. Association Rule Mining pected and achieved utilities from (5) and (6). 749 Proceedings of the 2008 IEEE IEEM TABLE II * ⎛⎞m =− TRUCK CAB AFFECTIVE DESIGN ELEMENTS uuij⎜⎟

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