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Linear Correlation Analysis of Numeric Attributes for GovernmentData

机译:政府数据数值属性的线性相关分析

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To analyze the linear correlations of numeric attributes of government data, this paper proposes a method based on the clustering algorithm. A clustering method is adopted to prune outliers and the linear correlation analysis is performed for each cluster, instead for the whole dataset. In this way, the method can obtain multiple correlations between the same two attributes. The paper presents the experiment on the government social security data. Experimental results show that the proposed method is much better than the traditional regression analysis and association rule analysis.
机译:为了分析政府数据的数字属性的线性相关性,本文提出了一种基于聚类算法的方法。采用群集方法对修剪异常值,对每个群集执行线性相关性分析,而是针对整个数据集执行。以这种方式,该方法可以在相同的两个属性之间获得多个相关性。本文提出了政府社会保障数据的实验。实验结果表明,该方法比传统回归分析和关联规则分析要好得多。

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