节点文献
聚类分析算法在电力营销决策支持系统中的应用研究
Research and Application of Clustering Algorithm on the Power Marketing Decision Support System
【作者】 程慈；
【导师】 柴瑞敏；
【作者基本信息】 辽宁工程技术大学 ， 计算机应用技术， 2008， 硕士
【摘要】 电力行业信息化建设在近几年得到飞速发展的同时,其数据却得不到很好地利用以支持决策。聚类分析是数据挖掘领域的重要分支,将其应用于电力行业,提升电力企业的市场竞争力。本文主要研究聚类分析算法在电力营销客户细分领域的应用。由于在细分客户的时候,并不能明确指出某个客户一定属于或不属于某一客户群体,恰恰相反一个客户样本可以属于不同的客户群,而模糊聚类算法由于引入隶属度的概念能更好地体现客户的这一特征,所以本文采用模糊c均值(FCM)聚类算法来对电力客户进行细分。在验证FCM聚类算法有效性时本文采用的是基于Xie-Beni有效性的方法,并对该方法进行了修改,另外又在FCM算法的循环之后添加了去除空簇的步骤,提高了算法的效率。在实验阶段,通过选用不同的参数细分来得到不同的细分结果,并对结果进行分析,验证了FCM算法的有效性;最后将改进后的FCM算法应用于电力营销决策支持系统中对客户进行细分。
【Abstract】 Informationization construction in electric power has been developed in recent years. Meanwhile,data of power plants cannot be well utilized to support decision.Clusteringanalysis, as an important embranchment of data mining,should be applied to electricpower industry, which will enhance market competitiveness of power plants.Along with restructuring of electric power system,the paces of the power supply enterprise undertaking marketing go faster. The thesis mainly researches on the application of clustering analysis in customer of electric marketing segmentation. It is hard to clearly say that a customer belongs to or does not belong to a particular customer group while segmenting customers. On the contrary, a customer can belong to different groups. Fuzzy c-means(FCM) clustering algorithm can commendably reflect the characteristics of customers for the introduction of membership concept, so fuzzy c-means(FCM) clustering algorithm is adopted here. The method used for validating the FCM validity is based on the Xie-Beni validity, the thesis modifies the Xie-Beni validity and a step of eliminating empty clusters is added after the FCM loop to improve the efficiency of the algorithm. In the experiment phase, we use then the validity of the algorithm is validated by analyzing these results. Finally,the improved FCM algorithm used in electric power marketing decision support system for customer analysis.