一种融合聚类和异常点检测算法的窃电辨识方法

聚类算法和异常点检测算法都是数据挖掘的重要方法。已有的聚类和异常点检测算法主要针对规律性数据挖掘，而没有将两种算法融合用于数据分析实现窃电辨识的方法。鉴于此，在分析相关算法原理和电量数据特征的基础上，提出一种融合聚类算法和异常点检测算法的窃电辨识方法，通过对电量异常数据的深入挖掘实现对窃电用户的准确辨识。理论分析和实验结果表明，该方法可有效提高窃电辨识的准确性，具有一定的实用性。

A Method of Stealing Identification Based on Fusion of Clustering and Abnormal-Point Detection Algorithm

Clustering algorithm and abnormal point detection algorithm are important methods of data mining. Existing clustering and anomaly detection algorithms are mainly aimed at regular data mining, and there is no method to integrate the two algorithms for data analysis to realize stealing identification. In view of this, a new stealing identification method combines clustering algorithm and anomaly detection algorithm is proposed based on the analysis that principle of the relevant algorithms and the characteristics of the electricity data, it realizes accurate identification of stealing users through the deep excavation of the abnormal data. Theoretical analysis and experimental results show that this method can effectively improve the accuracy of stealing identification, and has certain practicability.