Study On Extension Data Mining For Extension Architectural Programming

Extension Data Mining for Extension Architectural Programming(EapEdm)is interdisciplinary research of architecture,Extenics,and Data Mining.It is a significant component of the research project Study on theory and methods of Extension Data Mining for Extension Architectural Program and Design funded by the National Natural Science Foundation of China(No.51178132).In the Information Explosion age,it is common to see abundant data lacks useful knowledge in the architectural domain.Therefore,the aim of EapEdm is to utilize the advantages of Extension Data Mining(EDM)to complement the shortages of current theory and methods of Extension Architectural Programming(EAP),making EAP research towards a more scientific,efficient and intelligent way.EapEdm provides an automatic way to transform unlimited web data into useful knowledge supporting EAP.The thesis discusses the basic theory of EapEdm,data acquisition in EapEdm,database establishing,and knowledge discovery through a multidisciplinary perspective in which the theory and methods of EapEdm are built and then organized in four chapters.Chapter Two is the theoretical research of EapEdm.Based on current studies of EAP and Data Mining,the theory of EapEdm is further defined,providing foundations for future EapEdm applications research.In this chapter,Firstly,the meaning of EapEdm is defined to orient its location in EAP research,and the features of EapEdm are discussed.Secondly,four dimensions – time,environment,human,and innovation – are considered as the target of Data Mining.Thirdly,the process of undertaking EapEdm is established,which includes data acquisition,database establishing,and knowledge discovery.Finally,the computer implementation and the potential applications in the future are discussed in the chapter.Chapter Three is the data acquisition research of EapEdm.Based on the cloud platform and data acquisition software,it focuses on how to collect open web data in batch relating to architectural programming.In this chapter,the data acquisition principles and data processing procedures are proposed.Methods of data acquisition in relation to different kinds of data about architectural programming are discussed.These data include architectural industry dynamics data,architectural site environment data,architectural facility data and architectural images.After the process of data cleaning,data evaluation,and data transformation,these data are transformed into high qualified data,used for EapEdm research.Chapter Four is the database establishing of EapEdm.Based on current theories on database design,it focuses on establishing the database to organize,store and manage those data mentioned above.In this chapter,firstly,the survey was undertaken to understand the user requirements,classification of the database and function orientation from architectural scholars and architects.From four perspectives – conceptual design,logic design,function design,and database operation and maintenance,the Extension Data Mining of Extension Architectural Programming Database Management(EapEdm-DBMS)is built consisting of four kinds of database: architectural industry dynamics database,architectural site environment database,architectural facilities database,and architectural user requirements database.Chapter Five is the knowledge discovery research of EapEdm.Based on current studies of Data Mining and EDM,it proposes the Data Mining methods according to the problem modes of EAP.In this chapter,based on the EapEdm-DBMS associated with the knowledge requirements in EAP,different knowledge discovery methods are proposed for architectural industry dynamics data,architectural site environment data,architectural user requirements data,and architectural facilities data.The principles of each method are defined and explained through case studies.In short,through combing EAP and EDM,this research builds the theory of EapEdm and propose the methods of EapEdm for the knowledge discovery of EAP.By extending the applications of EDM,the new theory and methods provide architects with an intelligent tool to conduct architectural innovation through web data.