Exploring BIM Data by Graph-based Unsupervised Learning

Chaoyi Jin, Minyang Xu, Lan Lin, Xiangdong Zhou

Abstract

This paper presents an unsupervised learning method for mining the Industry Foundation Classes (IFC) based Building Information Modelling (BIM) data by exploring the inter-relational graph-like building spaces. In our method, the affinity propagation clustering algorithm is adapted with our proposed feature extraction algorithm to get exemplars of certain spaces with similar usage functions. The experiments are conducted on a real world BIM dataset. The experimental results show that some build spaces of typical usage functions can be discovered by our unsupervised learning algorithm.

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Paper Citation


in Harvard Style

Jin C., Xu M., Lin L. and Zhou X. (2018). Exploring BIM Data by Graph-based Unsupervised Learning.In Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-276-9, pages 582-589. DOI: 10.5220/0006715305820589


in Bibtex Style

@conference{icpram18,
author={Chaoyi Jin and Minyang Xu and Lan Lin and Xiangdong Zhou},
title={Exploring BIM Data by Graph-based Unsupervised Learning},
booktitle={Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2018},
pages={582-589},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006715305820589},
isbn={978-989-758-276-9},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Exploring BIM Data by Graph-based Unsupervised Learning
SN - 978-989-758-276-9
AU - Jin C.
AU - Xu M.
AU - Lin L.
AU - Zhou X.
PY - 2018
SP - 582
EP - 589
DO - 10.5220/0006715305820589