Zhou Wei, Wang Limin, Han Xuming, Wang Yizhang, Zhang Yufei, Jia Zhiyao
School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China.
School of Information, Guangdong University of Finance & Economics, Guangzhou 510320, China.
Entropy (Basel). 2023 May 11;25(5):782. doi: 10.3390/e25050782.
The density-based spatial clustering of application with noise (DBSCAN) algorithm is able to cluster arbitrarily structured datasets. However, the clustering result of this algorithm is exceptionally sensitive to the neighborhood radius () and noise points, and it is hard to obtain the best result quickly and accurately with it. To solve the above problems, we propose an adaptive DBSCAN method based on the chameleon swarm algorithm (CSA-DBSCAN). First, we take the clustering evaluation index of the DBSCNA algorithm as the objective function and use the chameleon swarm algorithm (CSA) to iteratively optimize the evaluation index value of the DBSCAN algorithm to obtain the best value and clustering result. Then, we introduce the theory of deviation in the data point spatial distance of the nearest neighbor search mechanism to assign the identified noise points, which solves the problem of over-identification of the algorithm noise points. Finally, we construct color image superpixel information to improve the CSA-DBSCAN algorithm's performance regarding image segmentation. The simulation results of synthetic datasets, real-world datasets, and color images show that the CSA-DBSCAN algorithm can quickly find accurate clustering results and segment color images effectively. The CSA-DBSCAN algorithm has certain clustering effectiveness and practicality.
基于密度的带噪声应用空间聚类（DBSCAN）算法能够对任意结构的数据集进行聚类。然而，该算法的聚类结果对邻域半径（）和噪声点异常敏感，难以快速准确地获得最佳结果。为了解决上述问题，我们提出了一种基于变色龙群算法（CSA-DBSCAN）的自适应DBSCAN方法。首先，我们将DBSCNA算法的聚类评估指标作为目标函数，利用变色龙群算法（CSA）迭代优化DBSCAN算法的评估指标值，以获得最佳值和聚类结果。然后，我们引入最近邻搜索机制的数据点空间距离偏差理论来分配识别出的噪声点，解决了算法噪声点过度识别的问题。最后，我们构建彩色图像超像素信息，以提高CSA-DBSCAN算法在图像分割方面的性能。合成数据集、真实世界数据集和彩色图像的仿真结果表明，CSA-DBSCAN算法能够快速找到准确的聚类结果，并有效地分割彩色图像。CSA-DBSCAN算法具有一定的聚类有效性和实用性。
