海量异构资源局部离群数据时间序列挖掘仿真OA

Due to the highly context dependent distribution of heterogeneous resource data,outlier points only appear as outliers within specific local neighborhoods.Traditional global anomaly detection methods assume that data follows a uniform or global distribution pattern,ignoring the significant differences in density across different regions.It leads normal data sensitive to local density fluctuations to be misclassified as anomalies,while genuine local anomalies are concealed by global statistical indicators.On this basis,a method of time series mining simulation for local outlier data of massive heterogeneous resources is proposed.Laplace mapping method is used to perform nonlinear dimensionality reduction on massive heterogeneous resource data,effectively reducing data dimensionality while preserving local structural information.Based on the reduced dimensional data,the fuzzy C-means clustering algorithm is used to calculate the dynamic time regularization distance with different time series,mine the time series patterns of heterogeneous resource data,and provide more accurate input for local anomaly detection.The local anomaly factor algorithm is introduced,and the local outlier factor is used to perform local anomaly detection on the time series mining results.Outliers within a specific local neighborhood are accurately identified by calculating the local anomaly factor of each data point.The simulation testing results show that the clustering entropy of this method for mining heterogeneous resource local outlier data time series is less than 0.2,which can accurately detect local outlier data.