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Early-stage ideation is a critical step in the design process. Mind maps are a popular tool for generating design concepts and in general for hierarchically organizing design insights. We explore an application for high-level concept synthesis in early stage design, which is typically difficult due to the broad space of options in early stages (e.g., as compared to parametric automation tools which are typically applicable in concept refinement stages or detail design). However, developing a useful mind map often demands a considerable time investment from a diverse design team. To facilitate the process of creating mind maps, we present an approach to crowdsourcing both concepts and binning of said concepts, using a mix of human evaluators and machine learning. The resulting computer-aided mind map has a significantly higher average concept novelty, and no significant difference in average feasibility (quantity can be set independently) as manually generated mind maps, includes distinct concepts, and reduces cost in terms of the designers’ time. This approach has the potential to make early-stage ideation faster, scalable and parallelizable, while creating alternative approaches to searching for a breadth and diversity of ideas. Emerging research explores the use of machine learning and other advanced computational techniques to amplify the mind mapping process. This work demonstrates the use of the both the EM-SVD, and HDBSCAN algorithms in an inferential clustering approach to reduce the number of one-to-one comparisons required in forming clusters of concepts. Crowdsourced human effort assists the process for both concept generation and clustering in the mind map. This process provides a viable approach to augment ideation methods, reduces the workload on a design team, and thus provides an efficient and useful machine learning based clustering approach.

Haixia Da,1 Qiaoliang Bao,2 Roozbeh Sanaei,1 Jinghua Teng,3 Kian Ping Loh,4,5 Francisco J. Garcia-Vidal,6 and Cheng-Wei Qiu1,* 1Electrical and Computer Engineering Department, National University of Singapore, 4 Engineering Drive 3, Singapore 117576, Singapore 2Department of Materials Engineering, Monash University, Clayton Campus, Clayton, Victoria 3800, Australia 3Institute of Materials Research and Engineering, Agency for Science, Technology, and Research (A∗STAR), 3 Research Link, Singapore 117602, Singapore 4Graphene Research Centre, National University of Singapore, 2 Science Drive 3, Singapore 117542, Singapore 5Department of Chemistry, National University of Singapore, 3 Science Drive 3, Singapore 117543, Singapore 6Departamento de Fisica Teorica de la Materia Condensada and Condensed Matter Physics Center (IFIMAC), Universidad Autonoma de Madrid, 28049 Madrid, Spain (Received 17 March 2013; revised manuscript received 15 October 2013; published 6 November 2013)