Application of Improved K-means Clustering Algorithm in Clustering Based on Power Customer Value
This paper uses an improved criterion based on K-means clustering algorithm applied in electric power custom?er clustering research. According to the characteristics of electricity customers to implement different marketing strategies and pro?vide differentiated services,accurate grouping of power customer need to be made. Traditional K-means clustering algorithm in data distribution uniform data of similar spherical agglomeration effect is better,once the unbalanced distribution density of data sets, class cluster size have significant difference,while the traditional K-means algorithm is easy to make thin categories carved up by high density small class clusters,resulting in electricity customer segmentation correct rate. This paper uses an improved K-means clustering algorithm based on the characteristics of the unbalanced data distribution of the actual power customers. Improved K-means algorithm puts up with a new weighting criteria,and modifies the clustering iterative process based on the criteria. The electricity customer data cluster results show that the improved K-means clustering algorithm and the cluster effect of each group of compactness can be improved effectively. The classification error conditions are improved obviously.
国家科技期刊平台
NSTL
万方数据
维普
