融合HHO算法和混合聚类算法的多源异构数据自动分类

Automatic Classification of Multi-Source Heterogeneous Data by Integrating HHO Algorithm and Hybrid Clustering Algorithm

摘要

针对多源异构数据的柔性重构需求需要处理数据细节和异常情况较多, 使其在对多源异构数据进行分类时, 难以通过捕获其隐藏特征, 整合多源异构数据的相似数据, 导致分类的准确性较低的问题, 提出融合HHO(Harris Hawks Optimization)算法和混合聚类算法的多源异构数据自动分类方法。利用低秩表示策略, 捕捉多源异构数据的隐藏特征, 获取其全局结构信息, 提高数据质量和一致性。组合蚁群聚类算法与K-means算法, 设计出混合聚类算法, 通过利用K-means算法修正组合蚁群聚类算法中的蚁巢消减误差, 实现多源异构数据的相似数据分组。将混合聚类算法与HHO算法结合, 通过更新个体适应度值, 确定HHO算法的最佳类中心, 实现相似分组后多源异构数据的自动分类。实验结果表明, 经分类结果的混淆矩阵和内聚度的评估结果共同验证, 所提方法不仅能自动分类多源异构数据, 且类别边界清晰, 数据点分布紧凑, 使混淆矩阵对角线上的真实值更高, 内聚度均值高达98.86%, 具有较高的分类准确性。

Abstract

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.

关键词

多源异构数据 / 低秩表示 / HHO算法 / 混合聚类算法 / 自动分类

Key words

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

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基金资助

克拉玛依市创新环境建设计划(软科学)基金资助项目(2024hjrkx0028)