基于SOM聚类的电网可视化数据挖掘模型

Visual Data Mining Model in Power Grid Based on SOM Clustering

面对电力系统中海量的多维数据,传统的可视化数据挖掘无法满足空间数据处理的需要,多维数据可视化也不利于用户获取知识。因此提出了基于SOM(自组织特征映射网络)聚类的电网可视化数据挖掘新模型VSDMmodel,模型利用改进的SOM聚类算法对高维电网数据进行降维,提出一种基于颜色映射的可视化方法,对聚类结果进行低维展现,加快了用户对挖掘结果的理解,并且允许用户对结果中感兴趣的区域加以深入分析,实现对电力系统海量数据的可视化挖掘。

The traditional visual data mining can＇t handle the spatial data when facing the huge amounts of multidimensional data in power system, and the multidimensional data visualization was also not conducive to collect the knowledge for the users. Therefore, the article put forward a new visualization of data mining model-VSDMmodel which based on SOM （Self-organizing feature map） clustering. The model used the improved SOM clustering algorithm to reduce the dimension of high dimensional data in grid and proposed a visualization method based on the color map to show the results in low-dimensional visualization. It can speed up the comprehension to the mining results; and it also allows users to analyze the interesting region in depth. Finally the model can mine the power system massive data in a visual method.