Science & Technology Review
|
2014, 32(36): 43-47
Methods for Extension Architectural Programming Classification Knowledge Mining on Parametric Data Set
Full
ZOU Guangtian, ZHANG Si, GUO Qiang, DING Lijuan
Affiliations
School of Architecture, Harbin Institute of Technology; Architectural Planning and Design Institute, Harbin Institute of Technology, Harbin 150006, China
Published: 2014-12-28
doi: 10.3981/j.issn.1000-7857.2014.36.006
Outline
Extension architectural programming classification knowledge mining on parametric data set is an application of extension data mining in the extension architectural programming area. However, the existing methods for classification knowledge mining are difficult to meet the particularity demands of extension architectural programming. The paper takes extension architectural programming data which can be set into parametric data set as a research object, and puts forward a procedure and certain methods for classification knowledge mining suitable for extension architectural programming, including pretreatment of data, selecting evaluation characteristics and determining the weights, establishing the correlation function and dividing the interval, and calculating and accessing the extension classification knowledge. The paper aims to expand the method-level research on extension architectural programming data mining to facilitate access to important extension architectural programming classification knowledge, and provides a new idea for computer-aided architectural programming.
extension architectural programming
/
extension data mining
/
extension classification knowledge
/
parametric data set
邹广天, 张斯, 郭强, 丁俐娟.
针对参变量数据元集的可拓建筑策划分类知识挖掘方法.
科技导报,
2014
, 32
(36)
: 43
-47
.
DOI: 10.3981/j.issn.1000-7857.2014.36.006
ZOU Guangtian, ZHANG Si, GUO Qiang, DING Lijuan.
Methods for Extension Architectural Programming Classification Knowledge Mining on Parametric Data Set[J].
Science & Technology Review,
2014
, 32
(36)
: 43
-47
.
DOI: 10.3981/j.issn.1000-7857.2014.36.006
Year 2014 volume 32 Issue 36
PDF
564
196
Cite this Article
BibTeX
Article Info
doi: 10.3981/j.issn.1000-7857.2014.36.006
- Receive Date:2014-10-23
- Online Date:2015-01-09
- Published:2014-12-28