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一种基于概念相似度的数据分类方法
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Supported by the National Natural Science Foundation of China under Grant No.60473071 (国家自然科学基金); the China Postdoctoral Science Foundation under Grant No.20060400002 (中国博士后科学基金); the Major Science and Technology Project of Sichuan Province of China under

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    摘要:

    依据数据属性间的相似信息,提出了一种分类方法.该方法将属性矢量化,属性作为m维空间的基本矢量,数据记录作为属性矢量的和.利用属性间先验的概念相似信息,给出了求取任意属性矢量对的相似距离算法,并将数据间相关度计算转换为属性矢量及其相互投影的公式,从而得到任意两条数据的相关度;利用相关度,提出了一种分类算法.用详实的实验证明了该算法的有效性.

    Abstract:

    In this paper, a method of classification is proposed based on the similar information of data properties. The new method assumes that data properties are basic vectors of m dimensions, and each of the data is viewed as a sum vector of all the property-vectors. It suggests a novel distance algorithm to get the distance of every pair of the property based on similar information of the basic property vectors. An algorithm of data classification is also presented based on correlation computing formula composed of property vectors and projections of each other. Efficiency of the new method is proved by extensive experiments.

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彭京,唐常杰,元昌安,李川,胡建军.一种基于概念相似度的数据分类方法.软件学报,2007,18(2):311-322

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  • 收稿日期:2004-09-08
  • 最后修改日期:2006-04-26
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