摘要
随着大数据时代的到来,数据的结构变得越来越复杂,数据集的维度变得越来越高,这极大地影响了数据挖掘的效率。因此,很有必要进行数据压缩或对信息系统进行属性约简,即去掉不必要的冗余属性,降低数据维度,提高数据挖掘效率。在现实生活中,人们对数据集中每个条件属性的关注度往往是不一样的。首先,在经典模糊决策数据集的基础上,对不同的条件属性进行加权,定义加权得分函数,进一步建立带关注度的模糊序决策信息系统。然后在该系统中引入分布函数,并通过分布可辨识矩阵建立求分布约简的方法。最后,通过案例分析验证了该方法的可行性。
With the advent of the era of big data,the structure of data becomes more and more complex,and the dimensions of data set become higher and higher,which will affect the efficiency of data mining greatly.Therefore,it is necessary to perform data compression or attribute reduction to information systems,that is,to remove unnecessary redundant attributes,reduce data dimensions,and improve the efficiency of data mining.The reduction methods proposed by many scholars in the past regard each attribute as equally important.But in real life,people’s attention to each conditional attribute in the data set is often different.Aiming at this phenomenon,based on the classical fuzzy decision data set,this paper weights different conditional attributes,defines the weighted score function,and further establishes the fuzzy order decision information system with attention degree.Then the distribution function is introduced into the system and the distribution reduction method is established by the distribution discernible matrix.Finally,the feasibility of the method is verified by a case study.
作者
徐伟华
张俊杰
陈修伟
XU Wei-hua;ZHANG Jun-jie;CHEN Xiu-wei(College of Artificial Intelligence,Southwest University,Chongqing 400715,China)
出处
《计算机科学》
CSCD
北大核心
2022年第S02期312-316,共5页
Computer Science
基金
国家自然科学基金面上项目(61976245)
关键词
模糊集
序决策数据集
关注度
分布约简
分布可辨识矩阵
Fuzzy set
Order decision data set
Attention degree
Distribution reduction
Distribution discernible matrix
