摘要
粗糙集理论处理高层建筑方案设计知识发现问题时,须对样本属性逐次处理,导致挖掘速度缓慢,针对这一缺点,采用一种基于概念普遍化和粗糙集的数据发掘算法,利用概念普遍化和粗糙集对数据进行压缩和维数精简的特长,利用知识归纳的方法压缩信息表中数据,再用标准粗糙集理论对表中数据进行属性约简、合并,从而导出决策规则.根据实际需要对信息表中的数据进行处理,达到高效发掘感兴趣模式的目的.并给出一个应用实例.
To discovery the problem while using the rough sets theory to solve the scheme design in high -rise building structures, we must deal with the samples＇attribute one by one, therefore, the velocity of data mining is slowly. To overcome this disadvantage, an algorithm based on concept generalization and the rough sets theory is presented, which can compress data and reduce dimension greatly. Firstly, we compress the data through concepts generalization, and then, we use rough set theory to reduce attributes and combine the same samples. In order to get knowledge required efficiently, we can dispose the information set according to practice requirement. Finally, an application example is presented to illustrate the feasibility and efficiency of the proposed method.
出处
《哈尔滨工业大学学报》
EI
CAS
CSCD
北大核心
2006年第12期2073-2076,共4页
Journal of Harbin Institute of Technology
基金
国家自然科学基金资助项目(50378030)
黑龙江省科技攻关项目(GC05A112)
黑龙江省博士后科研启动金资助项目(LHK-04072)
关键词
高层建筑结构
粗糙集
属性约简
high - rise building
rough sets
attribute reduction
