1 |
Park, I.-S., Park, N.-H., & Chun, H.-S. (2014). Changes in Apartment Unit Plan Caused by the Revision of Regulations for Area Calculating Criteria and Balcony Use: Focused on Changes of Size of Rooms in 60 m2 and 85 m2 Sized Unit. Journal of the Korean housing association, 25(2), 27-36. https://doi.org/10.6107/JKHA.2014.25.2.027
DOI
|
2 |
Ahn, E. (2021). Deep Learning Based Spatial Analysis Method for Korean Apartment Unit Plans [Doctoral dissertation, Seoul National University]. http://www.riss.kr/link?id=T15828388
|
3 |
Choi, J. (1990). From Courtyard to Living Room. Housing City, 51, 52-64.
|
4 |
Choi, J., Cho, H.-K., Park, I.-S., & Park, Y.-S. (2004). A Spatial Analysis of the Apartment Unit Plans from 1966 to 2002 in Seoul. Journal of the Architectural Institute of Korea Planning & Design, 20(6), 153-162.
|
5 |
Kluger, Y., Basri, R., Chang, J. T., & Gerstein, M. (2003). Spectral biclustering of microarray data: Coclustering genes and conditions. Genome Research, 13(4), 703-716. https://doi.org/10.1101/gr.648603
DOI
|
6 |
NAVER Corp. (2020). Naver Real Estate. https://land.naver.com/
|
7 |
Rodrigues, E., Sousa-Rodrigues, D., Teixeira de Sampayo, M., Gaspar, A. R., Gomes, A., & Henggeler Antunes, C. (2017). Clustering of architectural floor plans: A comparison of shape representations. Automation in Construction, 80, 48-65. https://doi.org/10.1016/j.autcon.2017.03.017
DOI
|
8 |
Yoshimura, Y., Cai, B., Wang, Z., & Ratti, C. (2019, July). Deep learning architect: classification for architectural design through the eye of artificial intelligence. In International Conference on Computers in Urban Planning and Urban Management (pp. 249-265). Springer, Cham.
|
9 |
Dhillon, I. S. (2001). Co-clustering documents and words using bipartite spectral graph partitioning. Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 269-274.
|
10 |
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. https://www.deeplearningbook.org/
|
11 |
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2015). Learning Deep Features for Discriminative Localization. http://arxiv.org/abs/1512.04150
|
12 |
Jeon, B., & Kwon, Y. (2012). Hanok and the history of the Korean house. Dongnyok Publishers. 197-200.
|
13 |
Kim, S., & Kim, S. (1997). Time series analysis of unit plan of private sector apartment housing in Korea. Housing Studies, 5(1), 103-127. http://www.riss.kr/link?id=A76459241
|
14 |
Kwon, H., Chun, W., & Park, J. (2006). A study on the efficient utilization plan of balcony space in apartment. Journal of the Architectural Institute of Korea Planning & Design, 26(1), 81-84.
|
15 |
Merrell, P., Schkufza, E., & Koltun, V. (2010). Computer-generated residential building layouts. ACM Transactions on Graphics, 29(6), 1. https://doi.org/10.1145/1882261.1866203
DOI
|
16 |
Rodrigues, E., Gaspar, A. R., & Gomes, A. (2013). An approach to the multi-level space allocation problem in architecture using a hybrid evolutionary technique. Automation in Construction, 35, 482-498.
DOI
|
17 |
scikit-learn developers. (2020). Biclustering. https://scikit-learn.org/stable/modules/biclustering.html
|
18 |
Seo, K. W. (2007). Space puzzle in a concrete box: finding design competence that generates the modern apartment houses in Seoul. Environment and Planning B: Planning and Design, 34(6), 1071-1084. https://doi.org/10.1068/b32134
DOI
|
19 |
Simonyan, K., & Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. http://arxiv.org/abs/1409.1556
|
20 |
Turner, A., Doxa, M., O'Sullivan, D., & Penn, A. (2001). From Isovists to Visibility Graphs: A Methodology for the Analysis of Architectural Space. Environment and Planning B: Planning and Design, 28(1), 103-121. https://doi.org/10.1068/b2684
DOI
|
21 |
Zeiler, M. D., & Fergus, R. (2014). Visualizing and understanding convolutional networks. European Conference on Computer Vision, 818-833.
|
22 |
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., & Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484-489. https://doi.org/10.1038/nature16961
DOI
|