【摘要】 为提高数据挖掘算法的挖掘速度,同时提高其精准度,提出基于电力营销聚类分析数据挖掘算法研究。首先运用聚类算法筛选数据,再计算数据结构和相异度矩阵相异度,得出最接近的类距离。完成上述步骤后,在聚类分析框架下,设计聚类分析数据挖掘算法流程。先输入数据,再设计数据挖掘算法基本策略,最后提出SLIO算法处理离散字段,得到有价值的数据信息。由此,完成基于电力营销聚类分析的数据挖掘算法设计。实验结果表明,与基于支持度-置信度-提升度的配网自动化系统数据挖掘算法和基于神经网络和粒子群优化的数据挖掘算法相比,文中基于电力营销聚类分析的数据挖掘算法的挖掘速度稳定,挖掘效果更好。同时测试精准度较高,可有效提高数据挖掘的可信度。更多还原

【Abstract】 In order to improve the speed and accuracy of data mining algorithm,a data mining algorithm based on power marketing clustering analysis is proposed.Firstly,the clustering algorithm is used to filter the data,and then the data structure,the dissimilarity matrix are calculated to get the closest class distance.After completing the above steps,under the framework of clustering analysis,the data mining algorithm flow of clustering analysis is designed.Input data,then design the basic strategy of data mining algorithm,and then put forward SLIO algorithm to deal with discrete fields to get valuable data information.Therefore,the design of data mining algorithm based on power marketing clustering analysis is completed.The experimental results show that the data mining algorithm based on power marketing cluster analysis has a stable mining speed and better mining effect compared with the data mining algorithm based on support confidence promotion degree and the data mining algorithm based on neural network and particle swarm optimization.At the same time,the test accuracy is high,which can effectively improve the credibility of data mining.更多还原