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research-article

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
zhangcw13@mails.tsinghua.edu.cn

Youngwook Paul Kwon

Department of Mechanical Engineering, University of California, Berkeley, 2114 Etcheverry, Berkeley, CA 94720
young@berkeley.edu

Julia Kramer

Department of Mechanical Engineering, University of California, Berkeley, 354/360 Hearst Memorial Mining Building, Berkeley, CA 94720
j.kramer@berkeley.edu

Euiyoung Kim

ASME Member, Jacobs Institute for Design Innovation, University of California, Berkeley, 2530 Ridge Road, Berkeley, CA 94720
euiyoungkim@berkeley.edu

Alice Agogino

ASME Fellow, Department of Mechanical Engineering, University of California, Berkeley, 415 Sutardja Dai Hall, Berkeley, CA 94720
agogino@berkeley.edu

1Corresponding author.

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

Abstract

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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