摘要
针对目前各领域数据的复杂性、概念边界的模糊性、需求的不确定性,该文提出了一种基于云模型的模糊数据挖掘分析方法,采用了云模型在定性语言值和定量数值之间的不确定转换模型,为定性与定量相结合的数据处理分析提供了有力的手段。其中包括:对数据进行概念和特征的模糊识别;建立隶属云模型,刻画数字特征;通过统计、计算、分析得到实际需求的分类信息。实验结果表明了该分析方法能在大量的复杂数据空间中挖掘出有价值的信息,符合实际应用。
Currently, in many fields, data is complicated; the boundary of concept is fuzzy; the demand is uncertain. Then a method of fuzzy data mining based on cloud model is proposed in this paper. This method adopt a uncertain transforming model between qualitative concepts and quantitative expressions, and provide an effective tool for data processing analysis combining quality with quantity. The content include: fuzzy identification of concepts and characteristic on the data; the establishment of membership cloud model and the depiction of digital characteristics; getting classified information based on actual demand by sta- tistics, calculating and analysis. The results of experiment show that the valuable information can be mined in the large and com- plex data space by this analysis method with practical significance.
作者
党辉
王治和
潘丽娜
DANG Hui, WANG Zhi-he, PAN Li-na (College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China)
出处
《电脑知识与技术》
2013年第2期870-872,共3页
Computer Knowledge and Technology
基金
甘肃省科技支援计划项目(090GKCA075)
2012年度教育部人文社会科学研究项目(12YJCZH282).
关键词
数据挖掘
云模型
隶属度
模糊概念
特征因子
定性与定量转换
data mining
cloud model
membership degree
fuzzy concept
characteristic factor
transformation between qualityand quantity