Improvement of DBSCAN Algorithm Based on K-Dist Graph for Adaptive Determining Parameters

数据库扫描 聚类分析 算法 计算机科学 图形 确定数据集中的群集数 模式识别(心理学) 航程(航空) 数学 人工智能 CURE数据聚类算法 相关聚类 材料科学 理论计算机科学 复合材料
作者
Lifeng Yin,Hongtao Hu,Kunpeng Li,Guanghai Zheng,Yingwei Qu,Huayue Chen
出处
期刊:Electronics [Multidisciplinary Digital Publishing Institute]
卷期号:12 (15): 3213-3213 被引量:4
标识
DOI:10.3390/electronics12153213
摘要

For the shortcomings of an unstable clustering effect and low accuracy caused by the manual setting of the two parameters Eps and MinPts of the DBSCAN (density-based spatial clustering of applications with noise) algorithm, this paper proposes an adaptive determination method for DBSCAN algorithm parameters based on the K-dist graph, noted as X-DBSCAN. The algorithm uses the least squares polynomial curve fitting method to fit the curve in the K-dist graph to generate a list of candidate Eps parameters and uses the mathematical expectation method and noise reduction threshold to generate the corresponding MinPts parameter list. According to the clustering results of each group of parameters in the Eps and MinPts parameter lists, a stable range of cluster number changes is found, and the MinPts and Eps corresponding to the maximum K value in the stable range are selected as the optimal algorithm parameters. The optimality of this parameter was verified using silhouette coefficients. A variety of experiments were designed from multiple angles on the artificial dataset and the UCI real dataset. The experimental results show that the clustering accuracy of X-DBSCAN was 21.83% and 15.52% higher than that of DBSCAN on the artificial and real datasets, respectively. The X-DBSCAN algorithm was also superior to other algorithms through comprehensive evaluation and analysis of various clustering indicators. In addition, experiments on four synthetic Gaussian datasets of different dimensions showed that the average clustering indices of the proposed algorithm were above 0.999. The X-DBSCAN algorithm can select parameters adaptively in combination with the characteristics of the dataset; the clustering effect is better, and clustering process automation is realized.
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