Uncovering design topics by visualizing and interpreting keyword data

This paper describes a bibliometric keyword analysis from the international DESIGN conference. We combined related keywords to form DESIGN topics. After that, we visualized the connections between the topics. Our analysis shows that the web of science database does not contain the DESIGN 2012-14 proceedings. That is relevant for the conference organizers, because content visibility is important. The topic visualization benefits both contributors to and organizers of the international DESIGN conference, because it shows trending topics and indicates areas with room for improvement.

[1]  David W. Rosen,et al.  REDEFINING PRODUCT FAMILY DESIGN FOR ADDITIVE MANUFACTURING , 2015 .

[2]  Ludo Waltman,et al.  Text mining and visualization using VOSviewer , 2011, ArXiv.

[3]  Arho Suominen,et al.  Modeling : Comparison of Unsupervised Learning and Human-Assigned Subject Classification , 2015 .

[4]  Gohar Feroz Khan,et al.  Information technology management domain: emerging themes and keyword analysis , 2015, Scientometrics.

[5]  James Allan,et al.  Introduction to topic detection and tracking , 2002 .

[6]  Ludo Waltman,et al.  Visualizing Bibliometric Networks , 2014 .

[7]  Seung Ki Moon,et al.  Decision Support Systems Design for Data-Driven Management , 2014, DAC 2014.

[8]  Byungun Yoon,et al.  A systematic approach for identifying technology opportunities: Keyword-based morphology analysis , 2005 .

[9]  Gohar Feroz Khan,et al.  Employee Engagement for Sustainable Organizations: Keyword Analysis Using Social Network Analysis and Burst Detection Approach , 2016 .

[10]  Louise Møller,et al.  SHARING THE DESIGN INTENT BETWEEN INDUSTRIAL DESIGNERS AND ENGINEERING DESIGNERS , 2016 .

[11]  Ludo Waltman,et al.  Software survey: VOSviewer, a computer program for bibliometric mapping , 2009, Scientometrics.

[12]  Li Sheng,et al.  Topic Detection and Tracking Review , 2007 .

[13]  Vladimir I. Levenshtein,et al.  Binary codes capable of correcting deletions, insertions, and reversals , 1965 .

[14]  Ed C. M. Noyons,et al.  A unified approach to mapping and clustering of bibliometric networks , 2010, J. Informetrics.

[15]  Trupti M. Kodinariya,et al.  Review on determining number of Cluster in K-Means Clustering , 2013 .

[16]  Martin Ester,et al.  Frequent term-based text clustering , 2002, KDD.

[17]  Eugene Garfield,et al.  From the science of science to Scientometrics visualizing the history of science with HistCite software , 2009, J. Informetrics.

[18]  Yi Zhang,et al.  Detecting and predicting the topic change of Knowledge-based Systems: A topic-based bibliometric analysis from 1991 to 2016 , 2017, Knowl. Based Syst..

[19]  H. Small,et al.  Identifying emerging topics in science and technology , 2014 .

[20]  K. G. Peeples,et al.  Mass customization: The new frontier in business competition: by B. Joseph Pine, II. Boston: Harvard Business School Press, 1993. 333+xvii pages. $29.95 , 1993 .