Partition KMNN-DBSCAN Algorithm and Its Application in Extraction of Rail Damage Data

数据库扫描 聚类分析 计算机科学 分拆(数论) 算法 数据挖掘 模式识别(心理学) 人工智能 数学 CURE数据聚类算法 相关聚类 组合数学
作者
Yujun Li,Zhi Kai Yang,Shangbin Jiao,Yuxing Li
出处
期刊:Mathematical Problems in Engineering [Hindawi Publishing Corporation]
卷期号:2022: 1-10 被引量:5
标识
DOI:10.1155/2022/4699573
摘要

In order to realize intelligent identification of rail damage, this paper studies the extraction method of complete damage ultrasonic B-scan data based on the density-based spatial clustering of applications with noise algorithm (DBSCAN). Aiming at the problem that the traditional DBSCAN algorithm needs to manually set the Eps and Minpts parameters, a KMNN-DBSCAN (K-median nearest neighbor DBSCAN) algorithm is proposed. The algorithm first uses the dataset’s own distribution characteristics to generate a list of Eps and Minpts parameters and then determines the optimal Eps and Minpts through an optimization strategy to achieve complete self-adaptation of the two parameters of Eps and Minpts. In order to further improve the clustering performance of the algorithm, the partition idea is introduced, and the partition KMNN-DBSCAN algorithm is proposed to solve the problem that the clustering results of the DBSCAN algorithm are inconsistent with the actual categories on datasets with uneven density. The experimental results show that the KMNN-DBSCAN algorithm has higher clustering accuracy and silhouette coefficient (SC) for the D037 dataset ultrasound information group (UIG) division; compared with the KMNN-DBSCAN algorithm, the proposed partition KMNN-DBSCAN algorithm has higher clustering accuracy, F-Measure, and SC values. The partition KMNN-DBSCAN algorithm achieves accurate division of all damage UIG on the damaged B-scan data with large density differences, and completes the effective extraction of complete damage data.
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