Wan Haidong, Lu Cheng, Cui Yongpeng
School of Information Science and Engineering, Southeast University, Nanjing 211189, China.
School of Electronic Engineering, Xidian University, Xian 710071, China.
Sensors (Basel). 2025 Aug 20;25(16):5184. doi: 10.3390/s25165184.
For the pulsed data streams emitted by multiple signal sources that generate aliasing, traditional density clustering algorithms have the problems of poor clustering effect, heavy reliance on manual experience to set the parameters, and the need to carry out density clustering every time new data are input, resulting in a huge amount of computation. Therefore, an online density clustering algorithm based on the improved golden sine whale optimization is proposed. First, by adding new parameters to the density clustering algorithm, the neighborhood is changed from a single parameter Eps to a joint decision of the parameters Eps and θ, which avoids cross-cluster expansion by more flexibly delimiting the neighborhood range. The improved golden sine whale optimization algorithm is then used to obtain the optimal parameter solution of the DBSCAN algorithm. Finally, the idea of flow clustering is introduced to determine whether a pulse belongs to a valid library, an outlier library, or an inactive library by comparing the distance between the input pulse and each cluster center, effectively reducing the number of pulses required for analysis. The experiment proves that the algorithm improves the sorting accuracy by 57.7% compared to the DBSCAN algorithm and 37.8% compared to the WOA-DBSCAN algorithm.
对于多个产生混叠的信号源所发射的脉冲数据流，传统密度聚类算法存在聚类效果差、严重依赖人工经验设置参数以及每次输入新数据时都需要进行密度聚类从而导致计算量巨大等问题。因此，提出了一种基于改进黄金正弦鲸鱼优化算法的在线密度聚类算法。首先，通过在密度聚类算法中添加新参数，将邻域从单一参数Eps转变为由参数Eps和θ的联合决策，通过更灵活地划定邻域范围避免了跨簇扩展。然后使用改进的黄金正弦鲸鱼优化算法来获得DBSCAN算法的最优参数解。最后，引入流聚类思想，通过比较输入脉冲与每个簇中心之间的距离来确定一个脉冲属于有效库、异常值库还是非活动库，有效减少了分析所需的脉冲数量。实验证明，该算法与DBSCAN算法相比，排序准确率提高了57.7%，与WOA-DBSCAN算法相比提高了37.8%。
