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计算机系统应用:2020,29(1):29-39
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基于自适应特征权重聚类算法的用电问题分析
(1.国网江苏省电力有限公司, 南京 210024;2.国网江苏省电力有限公司 电力科学研究院, 南京 210019;3.河海大学 计算机与信息学院, 南京 211100)
Electricity Consumption Problems Analysis Based on Adaptive Feature Weighted Clustering Algorithms
(1.State Grid Jiangsu Electric Power Co. Ltd., Nanjing 210024, China;2.Electric Power Research Institute, State Grid Jiangsu Electric Power Co. Ltd., Nanjing 210019, China;3.College of Computer and Information, Hohai University, Nanjing 211100, China)
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投稿时间:2019-06-18    修订日期:2019-07-16
中文摘要: 提升客服系统对于群体客户用电问题的分析与理解能力是改善电力行业客服质量的重要途径之一.本文基于数据挖掘中的聚类技术,以电力客服中心记录的客户用电问题为数据基础,建立客户服务数据分析聚类模型,进而提出了针对用电问题分析的改进的自适应特征权重K-Means聚类算法.实验验证了该方法可快速准确地实现客服数据的自动聚类,可挖掘出隐藏的客户用电问题关键信息,为改进用电力客服质量与潜在服务风险预测提供了技术支撑.
Abstract:Improving the analyzing and understanding ability of the customer service system for group customers' electricity consumption problems seems to be one of the important ways to improve the quality of customer service for power industry. Based on clustering technology in data mining, this study establishes a customer service data analysis clustering model for customers' electricity consumption problems recorded by a customer service center, and then proposes an improved adaptive feature weighted K-Means clustering algorithm for the analysis of electricity consumption problems. The experimental results show that the proposed method can quickly and accurately realize the automatic clustering of customer service data and mine the hidden critical information of customers' electricity consumption problems, thus providing technical support for improving the quality of customer service and predicting the potential risk of customer service.
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基金项目:国网江苏省电力有限公司科技项目(J2018020)
引用文本:
任禹丞,徐超,赵磊,贾静,彭路,周子馨.基于自适应特征权重聚类算法的用电问题分析.计算机系统应用,2020,29(1):29-39
REN Yu-Cheng,XU Chao,ZHAO Lei,JIA Jing,PENG Lu,ZHOU Zi-Xin.Electricity Consumption Problems Analysis Based on Adaptive Feature Weighted Clustering Algorithms.COMPUTER SYSTEMS APPLICATIONS,2020,29(1):29-39

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