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.

中文翻译：

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通过众包和机器学习生成计算机辅助思维导图

早期构思是设计过程中的关键步骤。思维导图是生成设计概念的流行工具，通常用于分层组织设计见解。我们探索了早期设计中高级概念综合的应用，由于早期阶段的选择空间广泛，这通常很困难（例如，与通常适用于概念细化阶段或详细设计的参数化自动化工具相比） . 然而，开发有用的思维导图通常需要多元化的设计团队投入大量时间。为了促进创建思维导图的过程，我们提出了一种众包概念和所述概念的分箱的方法，使用人类评估者和机器学习的组合。由此产生的计算机辅助思维导图具有明显更高的平均概念新颖性，与手动生成的思维导图在平均可行性（数量可以独立设置）上没有显着差异，包括不同的概念，并减少了设计人员的时间成本。这种方法有可能使早期构思更快、可扩展和可并行化，同时创建替代方法来搜索广泛和多样性的想法。新兴研究探索使用机器学习和其他先进的计算技术来放大思维导图过程。这项工作展示了 EM-SVD 和 HDBSCAN 算法在推理聚类方法中的使用，以减少形成概念聚类所需的一对一比较的数量。众包人力有助于思维导图中的概念生成和聚类过程。该过程提供了一种可行的方法来增强构思方法，减少设计团队的工作量，从而提供一种高效且有用的基于机器学习的聚类方法。