Application and evaluation of a K-Medoids-based shape clustering method for an articulated design space

Abstract

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.