A clustering-based method of typical architectural case mining for architectural innovation

被引:2
|
作者
Liu, Shuyu [1 ,2 ,3 ]
Zou, Guangtian [1 ,2 ,3 ]
Zhang, Si [1 ,2 ,3 ]
机构
[1] Harbin Inst Technol, Sch Architecture, 66 W Dazhi St, Harbin 150006, Peoples R China
[2] Minist Ind & Informat Technol, Key Lab Cold Reg Urban & Rural Human Settlement E, Harbin, Peoples R China
[3] Harbin Inst Technol, Architectural Planning & Design Inst, Harbin, Peoples R China
基金
中国国家自然科学基金;
关键词
Architectural innovation; design pattern; typical architectural case; intercase similarity; clustering; INTERRATER RELIABILITY; RETRIEVAL;
D O I
10.1080/13467581.2019.1709473
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Architectural innovation is important for improving the built environment. In recent years, an increasing number of architects have focused on this field. A comprehensive understanding of existing building design patterns contributes to reasonable innovation. Based on the large number of architectural cases on the internet in the big data era, this study proposes a three-stage case mining method, containing case collection, case analysis and case study, to find typical architectural cases, discover existing design patterns and create new design patterns by using cluster analysis of architectural cases. An extensive architectural design case mining system and a case clustering program are developed to assist in the case analysis. An agglomerative hierarchical clustering algorithm is applied to support the case clustering program. The example shows the complete application process and practical effect of the proposed method. With this intelligent method, architects can make more reasonable innovations in projects. The proposed typical case mining method is also expected to be useful for engineers and planners with similar needs.
引用
收藏
页码:71 / 89
页数:19
相关论文
共 50 条
  • [41] Fuzzy Clustering-Based Financial Data Mining System Analysis and Design
    Li, Kun
    Chen, Yanjun
    INTERNATIONAL JOURNAL OF FOUNDATIONS OF COMPUTER SCIENCE, 2022, 33 (06N07) : 603 - 624
  • [42] Understanding time use via data mining: A clustering-based framework
    Rosales-Salas, Jorge
    Maldonado, Sebastian
    Seret, Alex
    INTELLIGENT DATA ANALYSIS, 2018, 22 (03) : 597 - 616
  • [43] Clustering-based method for big spatial data partitioning
    Zein A.A.
    Dowaji S.
    Al-Khayatt M.I.
    Measurement: Sensors, 2023, 27
  • [44] An improved unsupervised clustering-based intrusion detection method
    Hai, YJ
    Wu, Y
    Wang, GY
    Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2005, 2005, 5812 : 52 - 60
  • [45] A clustering-based method for identifying and tracking squall lines
    Shi, Zhao
    Wen, Yuxiang
    He, Jianxin
    ATMOSPHERIC MEASUREMENT TECHNIQUES, 2024, 17 (13) : 4121 - 4135
  • [46] A clustering-based method to detect functional connectivity differences
    Chen, Gang
    Ward, B. Douglas
    Xie, Chunming
    Li, Wenjun
    Chen, Guangyu
    Goveas, Joseph S.
    Antuono, Piero G.
    Li, Shi-Jiang
    NEUROIMAGE, 2012, 61 (01) : 56 - 61
  • [47] A Clustering-based Method for Business Hall Efficiency Analysis
    Huang, Tianlin
    Wang, Ning
    SCIENTIFIC PROGRAMMING, 2021, 2021
  • [48] Semi-supervised clustering-based method for fault diagnosis and prognosis: A case study
    Azar, Kamyar
    Hajiakhondi-Meybodi, Zohreh
    Naderkhani, Farnoosh
    RELIABILITY ENGINEERING & SYSTEM SAFETY, 2022, 222
  • [49] A Clustering-Based Method for Intrusion Detection in Web Servers
    Pereira, Hermano
    Jamhour, Edgard
    2013 20TH INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS (ICT), 2013,
  • [50] A Hybrid Unsupervised Clustering-Based Anomaly Detection Method
    Guo Pu
    Lijuan Wang
    Jun Shen
    Fang Dong
    TsinghuaScienceandTechnology, 2021, 26 (02) : 146 - 153