Shape Organization for Enhanced Design Optimization in Generative Systems Using Clustering Technology

The current research in design space articulation tries to solve the problem that comes up because computer generative systems are becoming more complex, and there are more design options. This study makes a contribution to the existing body of research by introducing a technique for form clustering that utilizes K-medoids, a Machine Learning approach. The suggested method uses a clustering method to group design shapes that are similar and then pick a representative body for each cluster in a 2D-Grid Based representation. The efficacy of the process has been empirically validated by applying it to a novel test case and comparing it with other clustering evaluation methods. The accuracy of the results of this evaluation is about the same as that seen in an outside study. This gives us essential information about how to judge machine learning techniques.