计算机科学 ›› 2023, Vol. 50 ›› Issue (6A): 220400127-7.doi: 10.11896/jsjkx.220400127
张朋, 李小林, 王李妍
ZHANG Peng, LI Xiaolin, WANG Liyan
摘要： 传统的密度聚类算法在聚类划分时不会考虑数据点间的属性差异,它将所有数据点都看成同质化的点。对此,在DBSCAN算法的基础上,提出了一种动态邻域密度聚类算法DN-DBSCAN(Dynamic Neighborhood-Density Based Spatial Clustering of Applications with Noise)。该算法在聚类时由样本点的属性决定其自身的邻域半径,因此各点的邻域半径是动态变化的,由此可将具有不同属性的点对集群产生的不一样的影响力体现在聚类结果之中,使密度聚类算法更具有现实意义。在算例分析的基础上,针对长三角城市群划分问题应用所提DN-DBSCAN算法进行分析求解,并对比分析DBSCAN算法、OPTICS算法和DPC算法的求解效果。结果显示,DN-DBSCAN算法能根据各城市属性的不同合理地划分出长三角城市群,准确率为95%,准确率分别高于上述3种对比算法85%,85%,88%,说明其具有更好的解决实际问题的能力。
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