Optimization Algorithm for Time Series-Based Local Outlier Data Mining Optimization Algorithm
A local outlier data mining optimization algorithm based on time series is proposed to address the diffi-culty of outlier data mining caused by large data volumes and high data dimensions.Firstly,the global embedding al-gorithm based on angle optimization was combined with the common kernel principal component analysis method to form an AOCKPCA dimensionality reduction algorithm for reducing the dimensionality of massive high-dimensional time series.Then,the K-means algorithm was introduced to improve the computational efficiency of the ant colony al-gorithm and reduce the instability.Finally,the time series after dimension reduction was input into the optimized algo-rithm to realize the local outlier data mining.The experimental results show that the proposed method has high accura-cy in mining outlier data and a smaller number of misjudged outliers,indicating that its mining effect is better.
Time seriesLocal outlier data miningData dimensionality reductionAnt colony algorithm