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Application of Data Mining in Power Customer Satisfaction Evaluation

机译:数据挖掘在电力客户满意度评估中的应用

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In traditional power supply system, satisfaction data gained through customer revisit only been analyzed by on-line analytical processing(OLAP) method. Just as many other OLAP model, data used in the course of analysis are multidimensional, and most of potential values of data are difficult to be found in simple OLAP model based on multidimensional, data are need to be mined further so as to provide decision support to managers and decision-makers. This paper made research and elaboration to application of data mining technology in power customer relationship management(CRM). The entire model-building process adopted industrial recognized CRISP-DM data mining methodology with a certain degree of influences; the data mining algorithms used in this project were Clustering Algorithm and Principal Component Analysis; in the part of model verification, disordered matrix and income statement model were adopted.
机译:在传统的电源系统中,仅通过在线分析处理(OLAP)方法分析通过客户重新访问获得的满意度数据。就像许多其他OLAP模型一样,分析过程中使用的数据是多维的,并且在基于多维的简单OLAP模型中很难找到大多数数据潜在值,需要进一步挖掘数据以提供决策支持给经理和决策者。本文对数据挖掘技术在电力客户关系管理(CRM)中的应用进行了研究和阐述。整个模型构建过程均采用了行业认可的CRISP-DM数据挖掘方法,并具有一定程度的影响;该项目中使用的数据挖掘算法是聚类算法和主成分分析;在模型验证部分,采用无序矩阵和损益表模型。

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