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Shape Clustering Using K-Medoids in Architectural Form Finding

Part of the Communications in Computer and Information Science book series (CCIS,volume 1028)

Abstract

As the number of design candidates in generative systems is often high, there is a need for an articulation mechanism that assists designers in exploring the generated design set. This research aims to condense the solution set yet enhance heterogeneity in generative design systems. Specifically, this work accomplishes the following: (1) introduces a new design articulation approach, a Shape Clustering using K-Medoids (SC-KM) method that is capable of grouping a dataset of shapes with similitude in one cluster and retrieving a representative for each cluster, and (2) incorporate the developed clustering method in architectural form finding. The articulated (condensed) set of shapes can be presented to designers to assist in their decision making. The research methods include formulating an algorithmic set with the implementation of K-Medoids and other algorithms. The results, visualized and discussed in the paper, show accurate clustering in comparison with the expected reference clustering sets.

Keywords

  • Generative design systems
  • Clustering
  • Form finding
  • K-Medoids

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(image courtesy of Kalvelagen [6]).

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Correspondence to Shermeen Yousif .

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Yousif, S., Yan, W. (2019). Shape Clustering Using K-Medoids in Architectural Form Finding. In: Lee, JH. (eds) Computer-Aided Architectural Design. "Hello, Culture". CAAD Futures 2019. Communications in Computer and Information Science, vol 1028. Springer, Singapore. https://doi.org/10.1007/978-981-13-8410-3_32

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  • DOI: https://doi.org/10.1007/978-981-13-8410-3_32

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  • Print ISBN: 978-981-13-8409-7

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