Multi-attribute Hierarchical Clustering for Product Family Division of Customized Wooden Doors


  • Na Zhang College of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing 210037, China
  • Wei Xu College of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing 210037, China
  • Yong Tan Department of Product Research and Development, ZBOM Home Collection Co., Ltd., Hefei 230001, China


Customized furniture, Product system optimization, Machine learning, Research and development


To improve the production system for customized wooden doors and to gain research and development efficiency, this paper proposed the feasibility of using hierarchical clustering algorithms to cluster a company's customized wooden door products and its application to rational product family architecture. The particular use of multi-attribute feature data to locate products and the integration of image data into the database can make the original hierarchical clustering more compatible and adaptable for application to customized wooden doors. The preprocessed data was analyzed by clustering to obtain the clustering results and similarity relationships. Hierarchical clustering results were uneven and not entirely interpreted. However, the internal order structure of clusters and the clustering process could be clearly observed, and the distance hierarchical relationship between the products could be obtained, which was beneficial to the division of the product architecture. The results illustrated that processing using hierarchical clustering of multi-attribute data is feasible for optimizing customized wooden door product systems. In addition, the product architecture, product coding rules, and front-end development process were established to improve standardization and research and development efficiency. There is still great potential for developing the custom wooden door category in custom furniture companies.






Research Article or Brief Communication