基于改进AdaBoost的密度峰值聚类法

Density Peaks Clustering based on optimized AdaBoost algorithm

针对雷达数据集中目标和杂波点迹的聚类不平衡问题,提出一种基于改进AdaBoost的密度峰值聚类法。介绍密度峰值聚类法的思想,基于不对称误分代价改进AdaBoost的误差函数,提高正类错分代价权重,将改进AdaBoost和密度峰值聚类结合,对由目标和杂波点迹组成的不平衡雷达数据集聚类。仿真实验结果表明,该算法在保证总体聚类性能的同时提高对正类的识别。

A Density Peaks Clustering algorithm based on optimized AdaBoost-DPC is proposed to deal with the class-imbalanced question of target and clutter in radar data sets.The method of density peaks clustering is introduced,and the clutter dots are under-sampled.The error function of AdaBoost algorithm is improved based on the asymmetric misclustering cost,which raises the weight of positive misclassification cost.Then the improved AdaBoost algorithm is combined with density peaks clustering method to cluster the imbalanced radar data sets consisting of the targets and clutter dots.The experimental results show that the optimized method can effectively improve the identification of target.