Application of an Automatic Shape Clustering Method (2019)
article⁄Application of an Automatic Shape Clustering Method (2019)
abstract⁄Despite their prevalence and extensive applications, generative and design optimization systems lack effective organizational methods of the excessive number of design options they produce, which is problematic for designers’ interaction. Ideally, a diverse and organized set of designs can mediate successful designers’ evaluation and exploration of the design space. Cluster analysis, a bigdata management strategy, offers a solution. Yet, there is a need for investigating appropriate methods for applying clusteranalysis to a dataset of geometric shapes. Therefore, we have recently developed and published a new approach, the Shape Clustering using KMedoids SCKM method as an articulation mechanism in generative systems. The method involves shape description, shape difference measure calculation, and implementation of the KMedoids clustering algorithm. The focus of this work is on incorporating the method into a generative system with parametric building shape generation and design optimization. The method organizes a dataset of shapes into clusters where shapes within the cluster share similarities yet differ from other clusters, and each cluster is signified by one representative shape. The SCKM method contributes to an organized design presentation and facilitates designers’ examination of their designs’ geometric qualities.
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| Year |
2019 |
| Authors |
Yousif, Shermeen; Yan, Wei. |
| Issue |
ACADIA 19:UBIQUITY AND AUTONOMY |
| Pages |
60-69 |
| Library link |
N/A |
| Entry filename |
application-automatic-shape-clustering-method |
