王孝慈,董树锋,刘育权,王莉,李俊格.基于改进式k-prototypes聚类的坏数据辨识与修正[J].电测与仪表,2022,59(2):9-15. Wang Xiaoci,Dong Shufeng,Liu Yuquan,Wang Li,Li Junge.Bad data identification and correction method based on improved k-prototypes clustering[J].Electrical Measurement & Instrumentation,2022,59(2):9-15.
基于改进式k-prototypes聚类的坏数据辨识与修正
Bad data identification and correction method based on improved k-prototypes clustering
The realization of many technologies in the industrial field is based on accurate load data, while the existing measurement system in factories often results in a large number of bad data due to communication and storage failures. Therefore, an industrial load data identification and correction method based on improved k-prototypes clustering algorithm is proposed to reduce the impact of bad load data on the clustering results by introducing characteristics of non-load data in clustering, so as to make the identification and repair results more accurate. Through random initialization and parallel calculation optimization, the improved k-prototypes algorithm overcomes the defect that standard algorithm tends to fall into the local optimal solution. And the problem of subjectively determining the number of clusters is solved by adaptive processing. Based on the clustering results, the feasible region of load data is determined according to the principle of normal distribution, and the bad data is identified. The identified bad data is corrected by centroid vector replacing. Experiments show that the proposed method outperforms the fuzzy C-means clustering method which only considers the load data, and the recall rate and correction accuracy of bad data identification are significantly improved.
