Research on articulating the design space in computational generative systems is ongoing, to overcome the issue of possible overwhelming multiplicity and redundancy of emerging design options. The article contributes to this line of research of design space articulation, in order to facilitate designers’ successful exploration in computational design. We have recently developed a method for shape clustering using K-Medoids, a machine learning-based strategy. The method performs clustering of similar design shapes and retrieves a representative shape for each cluster in 2D grid-based representation. In this paper, we present a progress in our project where the method has been applied to a new test case, and empirically verified using clustering evaluation methods. Our clustering evaluation results show comparable accuracy when assessed against an external study and provide insight into the evaluation criteria for machine learning methods, as presented in the paper.

中文翻译：

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基于K-Medoids的形状聚类方法在关节设计空间中的应用和评估

正在进行关于在计算生成系统中表达设计空间的研究，以克服新兴设计方案可能产生压倒性的多重性和冗余性的问题。本文为设计空间清晰度的这一研究领域做出了贡献，以促进设计人员在计算设计中的成功探索。我们最近开发了一种使用K-Medoids（一种基于机器学习的策略）进行形状聚类的方法。该方法对相似设计形状进行聚类，并在基于2D网格的表示中为每个聚类检索代表性形状。在本文中，我们介绍了该项目的进展，该方法已应用于新的测试案例，并使用聚类评估方法进行了实证验证。