Research On Product Design Method For User Cluster

With the great enrichment of products,the demand of users has became the focus of product design.To accurately obtain fuzzy and complex user requirements has bring a good theoretical reference and practical application value for the design positioning and decision-making in the product development process.It is also the key for enterprises to accurately grasp the market trend and ensure product competitiveness in the fierce market competition and it also proposes a product design method for user cluster to make the product design scheme can better meet the needs and preferences of target users.To subdivided users based on feature attributes from user characteristics,so that the user cluster can be completely constructed,and the diversified,differentiated and personalized demand preferences from the perspective of cluster are mined to obtain the representative design scheme of cluster.The main research content includes the following three parts:First,extracted user major characteristics that influence demand preferences.Through the analysis of the target users,to mined the physiological,emotional and social characteristics that influence the demand preference,evaluated and decided to the primary characteristics of the users by means of expert research.The user characteristics can be reduced and screened by the rough numbers,and the main characteristics that affect demand preferences can be obtained.Second,built a user cluster based on the main characteristics.Analyzed the physiological,emotional,social characteristics of identified users and the user feature information of the target group are obtained through different modes.The k-modes algorithm is used to conduct user segmentation of the target group based on the user feature attributes,so that the user cluster can be completely constructed.Third,designed and applied products tailored to demand preference of user cluster.Through the target product samples of the established user cluster are collected,the morphological of the product samples are analyzed.The information entropy model is used to calculate the information entropy of single design elements and mutual information of two design elements in the cluster,thus we gained the preference degree of the cluster to the design elements of the target products.According to the preference of user cluster,the design elements are reconstructed to obtain the product scheme cluster oriented to user cluster,and introduced theK-Nearest-Neighbor algorithm into the new users cluster to evaluate of a product scheme.The research is based on the eyeglasses' example,taking college students as the target groups.According to the differences of characteristics of college students,objectively construct user clusters based on feature attributes,and analyze cluster demand preferences.Thus the product can be designed according to the differences and the products can be fully meet the needs of the target group.