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
  • [21] Optimization Strategy of Architectural Design Based on Data Mining
    Sun Y.
    Xu Z.
    Zhong J.
    Computer-Aided Design and Applications, 2024, 21 (S19): : 275 - 292
  • [22] The interaction based innovation process of architectural design service
    Zhang, Jingbo
    Tao, Yan
    2007 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT, VOLS 1-4, 2007, : 1719 - +
  • [23] CASE-STUDY METHOD + ARCHITECTURAL EDUCATION
    DIPASQUALE, R
    PROGRESSIVE ARCHITECTURE, 1990, 71 (05): : 63 - 63
  • [24] Optimized Hypergraph Clustering-based Network Security Log Mining
    Che, Jianhua
    Yu, Yong
    Lin, Weimin
    Yao, Wei
    2010 INTERNATIONAL CONFERENCE ON COMMUNICATION AND VEHICULAR TECHNOLOGY (ICCVT 2010), VOL II, 2010, : 192 - 195
  • [25] Optimized Hypergraph Clustering-based Network Security Log Mining
    Che, Jianhua
    Lin, Weimin
    Yu, Yong
    Yao, Wei
    INTERNATIONAL CONFERENCE ON APPLIED PHYSICS AND INDUSTRIAL ENGINEERING 2012, PT A, 2012, 24 : 762 - 768
  • [26] Clustering-based multidimensional sequential pattern mining of semantic trajectories
    Sakouhi, Thouraya
    Akaichi, Jalel
    INTERNATIONAL JOURNAL OF DATA MINING MODELLING AND MANAGEMENT, 2024, 16 (02) : 148 - 175
  • [27] An Effective Clustering-based Approach for Conceptual Association Rules Mining
    Quan, Tho T.
    Ngo, Linh N.
    Hui, Siu Cheung
    2009 IEEE-RIVF INTERNATIONAL CONFERENCE ON COMPUTING AND COMMUNICATION TECHNOLOGIES: RESEARCH, INNOVATION AND VISION FOR THE FUTURE, 2009, : 257 - +
  • [28] Implementation of a Clustering-Based LDDoS Detection Method
    Hussain, Tariq
    Saeed, Muhammad Irfan
    Khan, Irfan Ullah
    Aslam, Nida
    Aljameel, Sumayh S.
    ELECTRONICS, 2022, 11 (18)
  • [29] A clustering-based method for unsupervised intrusion detections
    Jiang, SY
    Song, XY
    Wang, H
    Han, JJ
    Li, QH
    PATTERN RECOGNITION LETTERS, 2006, 27 (07) : 802 - 810
  • [30] Fuzzy clustering-based on aggregate attribute method
    Wang, Jia-Wen
    Cheng, Ching-Hsue
    ADVANCES IN APPLIED ARTIFICIAL INTELLIGENCE, PROCEEDINGS, 2006, 4031 : 478 - 487