计算机与数字工程2017，Vol.45Issue(6)：1049-1054,1149,7.DOI:10.3969/j.issn.1672-9722.2017.06.008
基于改进K-means聚类算法在电力客户价值分群的应用
Application of Improved K-means Clustering Algorithm in Clustering Based on Power Customer Value
朱州 1吴漾1
作者信息
- 1. 贵州电网有限责任公司信息中心 贵阳 550003
- 折叠
摘要
Abstract
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.关键词
K-means算法/新聚类准则/迭代权重/正确率/标准差Key words
K-means algorithm/new clustering criterion/iterative weight/correct rate/standard deviation分类
信息技术与安全科学引用本文复制引用
朱州,吴漾..基于改进K-means聚类算法在电力客户价值分群的应用[J].计算机与数字工程,2017,45(6):1049-1054,1149,7.
