Concept Clustering in Design Teams: A Comparison of Human and Machine Clustering

[+] Author and Article Information
Chengwei Zhang

Department of Mechanical Engineering, Tsinghua University, A1003-1 Lizhaoji, Tsinghua University, Beijing, China, 100084

Youngwook Paul Kwon

Department of Mechanical Engineering, University of California, Berkeley, 2114 Etcheverry, Berkeley, CA 94720

Julia Kramer

Department of Mechanical Engineering, University of California, Berkeley, 354/360 Hearst Memorial Mining Building, Berkeley, CA 94720

Euiyoung Kim

ASME Member, Jacobs Institute for Design Innovation, University of California, Berkeley, 2530 Ridge Road, Berkeley, CA 94720

Alice Agogino

ASME Fellow, Department of Mechanical Engineering, University of California, Berkeley, 415 Sutardja Dai Hall, Berkeley, CA 94720

1Corresponding author.

ASME doi:10.1115/1.4037478 History: Received February 20, 2017; Revised July 18, 2017


Concept clustering is an important element of the product development process. The process of reviewing multiple concepts provides a means of communicating concepts developed by individual team members and by the team as a whole. Clustering, however, can also require arduous iterations and the resulting clusters may not always be useful to the team. In this paper, we present a machine learning approach on natural language descriptions of concepts that enables an automatic means of clustering. Using data from over 1,000 concepts generated by student teams in a graduate new product development class, we provide a comparison between the concept clustering performed manually by the student teams and the work automated by a machine learning algorithm. The goal of our machine learning tool is to support design teams in identifying possible areas of "over-clustering" and/or "under-clustering" in order to enhance divergent concept generation processes.

Copyright (c) 2017 by ASME
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