An efficient DBSCAN optimized by arithmetic optimization algorithm with opposition-based learning

Abstract

As unsupervised learning algorithm, clustering algorithm is widely used in data processing field. Density-based spatial clustering of applications with noise algorithm (DBSCAN), as a common unsupervised learning algorithm, can achieve clusters via finding high-density areas separated by low-density areas based on cluster density. Different from other clustering methods, DBSCAN can work well for any shape clusters in the spatial database and can effectively cluster exceptional data. However, in the employment of DBSCAN, the parameters, EPS and MinPts, need to be preset for different clustering object, which greatly influences the performance of the DBSCAN. To achieve automatic optimization of parameters and improve the performance of DBSCAN, we proposed an improved DBSCAN optimized by arithmetic optimization algorithm (AOA) with opposition-based learning (OBL) named OBLAOA-DBSCAN. In details, the reverse search capability of OBL is added to AOA for obtaining proper parameters for DBSCAN, to achieve adaptive parameter optimization. In addition, our proposed OBLAOA optimizer is compared with standard AOA and several latest meta heuristic algorithms based on 8 benchmark functions from CEC2021, which validates the exploration improvement of OBL. To validate the clustering performance of the OBLAOA-DBSCAN, 5 classical clustering methods with 10 real datasets are chosen as the compare models according to the computational cost and accuracy. Based on the experimental results, we can obtain two conclusions: (1) the proposed OBLAOA-DBSCAN can provide highly accurately clusters more efficiently; and (2) the OBLAOA can significantly improve the exploration ability, which can provide better optimal parameters.

A Review of Uncertainty-Based Multidisciplinary Design Optimization Methods Based on Intelligent Strategies

“…In addition to the k-means and GMM methods, there are many other types of partitionbased clustering algorithms. For example, centroid-based algorithms like centroid-based algorithm with KL-divergence clustering [147], graph-based clustering algorithms with spectral clustering [148] and robust continuous clustering [149], and density-based algorithms, such as density-based spatial clustering of applications with a noise algorithm [150]. Each of these algorithms has its own advantages and disadvantages.…”

A Spark-Based Parallel Implementation of Arithmetic Optimization Algorithm

“…Though the original AOA has shown considerable gains in performance against current metaheuristics, it may suffer from slow convergence, getting stuck in local optima, and inadequate exploitation. To further enhance AOA performance, researchers have proposed a variety of stochastic operators such as opposition-based learning (Abualigah et al, 2022;Yang et al, 2022), and Gaussian mutation mechanism and sinusoidal chaotic map (Xu et al, 2021) to address such issues. In another enhancement, such as in (Wang et al, 2021) AOA population is divided among multiple groups, where each group operates independently and exchanges information among randomly selected groups after fixed number of iterations.…”

WOA-DBSCAN: Application of Whale Optimization Algorithm in DBSCAN Parameter Adaption

“…However, the algorithm has two problems: the search boundary is not determined, and the number of iterations is too many. Yang et al [33] used a novel meta-heuristic algorithm-arithmetic optimization method. They combined it with oppositional learning to implement an algorithm for DBSCAN parameter optimization (OBLAOA-DBSCAN), which features fast convergence speed and high accuracy through a mathematical optimizer that selects different optimization strategies at initialization and gradual convergence.…”