Clustering Sequence Outlier Data Mining Algorithm Based on Machine Learning
Due to the temporal dependency of clustering sequence outlier data,it is difficult to accurately detect outliers,resulting in unsatisfactory data mining.Clustering sequence outlier data mining algorithm based on machine learning is proposed to address this issue.Using machine learning methods to cluster outlier data in clustering sequences calculates the outlier index.Aggregating data through machine learn-ing and assigning outlier data,the research traverses the feature sequence of data samples,calculates the applicability of feature intervals,and analyzes the relationship between features and target variables.The research transforms the data classification mining problem into a linearly separable problem to avoid over-fitting,designing a data mining process that records the timestamp of each data point to achieve data min-ing.From the experimental results,it can be seen that the algorithm only has 1%error in the proportion of outliers between the PSLG dataset and the actual ones,while the rest are consistent.The data mining range is consistent with the calibration range,and it has a precise mining effect.
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