基于K-mean聚类算法的电力营销数据分析

Analysis of Power Marketing Data Based on K-mean Clustering Algorithm

阐述运用BP神经网络,计算所有层的误差函数,在指定的范围内进行样本训练获取权重与阈值,计算矩阵的特征向量并排列成新矩阵,获取特征值和阈值,计算样本到聚类中心的距离得到最优聚类数数据,从而实现基于K-mean聚类算法的电力营销数据分析。

This paper expounds the use of BP neural networks to calculate the error functions of all layers,train samples within a specified range to obtain weights and thresholds,calculate the eigenvectors of the matrix and arrange them into a new matrix,obtain eigenvalues and thresholds,calculate the distance from the sample to the cluster center to obtain the optimal number of clusters,and thus achieve power marketing data analysis based on K-mean clustering algorithm.