Due to the flexible reconstruction requirements of multi-source heterogeneous data, which require handling of data details and abnormal situations, it is difficult to classify multi-source heterogeneous data by capturing its hidden features and integrating similar data, resulting in low classification accuracy. Therefore, a multi-source heterogeneous data automatic classification method that integrates HHO(Haris Hawks Optimization) algorithm and hybrid clustering algorithm is proposed. Using low rank representation strategy to capture hidden features of multi-source heterogeneous data, its global structural information is obtained, and data quality and consistency are improved. A hybrid clustering algorithm is designed by combining ant colony clustering algorithm and K-means algorithm. By using K-means algorithm to correct the ant nest reduction error in the combined ant colony clustering algorithm, similar data grouping of multi-source heterogeneous data is achieved. Combining hybrid clustering algorithm with HHO algorithm, by updating individual fitness values, the optimal class center of HHO algorithm is determined to achieve automatic classification of multi-source heterogeneous data after similar grouping. The experimental results show that the proposed method can automatically classify heterogeneous data from multiple sources and has clear category boundaries and compact data point distribution, resulting in higher true case values on the diagonal of the confusion matrix. The average cohesion is as high as 98. 86°,demonstrating high classification accuracy.

multi-source heterogeneous data, low rank representation, Harris Hawks optimization ( HHO) algorithm, hybrid clustering algorithm, automatic classification