Abstract：In order to improve the accurate detection and recognition ability of abnormal data in ground meteorological observation sets,a ground meteorological observation data anomaly mining method based on FP-Growth algorithm is proposed.Set the frequency of data collection,determine the time interval for data collection based on observation needs,and integrate ground meteorological observation data for sampling;Introducing the FP-Growth algorithm,based on the FP-Tree structure,to screen frequent terms and extract features from observed data.For each point in the dataset,calculate the distance to its nearest neighbor,define the anomaly score based on the distance,and achieve anomaly data mining and clustering.The experimental results show that the abnormal amount of observation data mined by the design method is consistent with the actual sample data,indicating that this method can achieve accurate mining of ground meteorological observation data anomalies in practical applications.
许烨, 牛淑丽, 狄增文. 基于FP-Growth算法的地面气象观测数据异常挖掘[J]. 气象水文海洋仪器, 2025, 42(1): 33-36.
Xu Ye, Niu Shuli, Di Zengwen. Anomaly mining of ground meteorological observation data based on FPGrowth algorithm. Meteorological Hydrological and Marine Instrument, 2025, 42(1): 33-36.
2025, Vol. 42 
