Condensing Complexity
- K-means clustering of geometric data sets as a design tool
Authors
Date
03 September 2025
Keywords
Architecture, Construction Engineering, Computer Sciences
ISBN/ISSN/ISMN, DOI
- ISBN/ISSN/ISMN:
- 978-9-49120-739-6
Text
This paper explores the use of K-means clustering as a design tool to manage geometric complexity in design contexts. While computational design has often been associated with formal complexity, this work investigates techniques for condensing complexity and structuring irregular datasets into meaningful clusters to enable systematic design interventions. By applying K-means clustering to irregular geometries, the paper demonstrates how digital abstraction can serve as an alternative to physical standardization, preserving material and/or geometric specificity in structured assemblies. Three case studies illustrate different approaches to integrating clustering within design systems, highlighting its potential to enable informed design decisions.
Volume/Issue, Pages
Volume 1, 297-305
Language, Format, Material, Edition
English
Activity List
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- Arzu Gönenç Sorguç, Müge Kruşa Yemişcioğlu , Serda Buket Erol, Mustafa Eren Bük, Dilara Güney, Betül Aktaş Sulayıcı, Mert Akol: eCAADe 2025 - Confluence. Proceedings of the 43rd Conference on Education and Research in Computer Aided Architectural Design in Europe. eCAADe (Education and research in Computer Aided Architectural Design in Europe), Middle East Technical University Faculty of Architecture, Ankara. 2025-09-03 -
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