基于AHP和CRITIC综合赋权的K-means算法
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K-Means Algorithm Based on Synthetic Weighting of AHP and CRITIC
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    摘要:

    传统的K-means算法认为被分析样本的各个属性在聚类中作用是相同,针对这种不足,提出一种基于AHP和CRITIC综合赋权的K-means聚类算法.首先利用CV-K-means方法计算每个属性的权重,从而两两进行比较得到判断矩阵.然后,根据层次分析法(AHP)确定各个属性的主观权重,再利用CRITIC方法确定各个属性的客观权重.采用差异系数法确定组合系数,实验证明该算法的聚类精确度高于传统K-means算法.

    Abstract:

    The traditional K-means algorithm is regarded that the attributes of swatches have the same effect on the clustering analysis. Based on AHP and CRITIC, comprehensive weighting of K-means clustering algorithm is proposed to solve the problem in this paper. First, each of attribute weight is calculated by CV-K-means method, thus judgment matrix is determined by comparing the two.Then, according to the analytic hierarchy process subjective weights of attributes is determined. And using the CRITIC method the objective weight of each attribute is determined, difference coefficient method is used to determine coefficient of combination. The experimental results show that the algorithm accuracy is higher than the traditional K-means algorithm.

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丁晓琴,张德生.基于AHP和CRITIC综合赋权的K-means算法.计算机系统应用,2016,25(7):182-186

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  • 收稿日期:2015-11-24
  • 最后修改日期:2016-01-07
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  • 在线发布日期: 2016-07-21
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