微型电脑应用

为了充分挖掘电力系统营销数据内在联系,提高电力客户动态细分精度,设计了一种大数据下的电力客户动态细分方法。首先运用熵值法和主成分分析的方法确定影响分类结果较大的因素指标,然后用无监督改进的K-means算法进行客户数据细分,同时运用K近邻算法对细分模型进行检验,可以对指定客户进行精准分类,最后通过价值客户细分结果,分析各类群体行为特征,提出差异化营销策略,提升供电公司营业水平。结果表明,提出的方法提高了电力客户动态细分精度,降低了电力客户动态细分误差,具有一定的实际应用价值。

In order to fully tap the internal relationship of power system marketing data and improve the accuracy of dynamic customer segmentation, a dynamic customer segmentation method based on large data is designed. Firstly, the methods of entropy value and principal component analysis are used to determine the factors that affect the classification results. Then, unsupervised improved K-means algorithm is used to segment customer data. At the same time, K-nearest neighbor algorithm is used to test the segmentation model, which can accurately classify designated customers. Finally, through the value customer segmentation results, the characteristics of various groups' behavior are analyzed and the differentiation is proposed. The results show that the presented method improves the dynamic subdivision accuracy of power customers and reduces the dynamic subdivision error of power customers, which has a certain practical application value.