DBSCAN ship abnormal behavior identification based on improved Hausdorff distance (with Matlab code)
The identification of abnormal ship behavior is one of the important issues in the field of ship monitoring and safety. In this article, we will introduce a DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm based on the improved Hausdorff distance for identification of abnormal ship behavior. We also provide the corresponding Matlab code to facilitate readers to practice and understand the implementation process of the algorithm.
DBSCAN is a density-based clustering algorithm that can discover data points with similar densities and form them into clusters. Modified Hausdorff distance is a method used to measure the distance between two point sets and can effectively identify abnormal behavior.
The following is the Matlab code for DBSCAN ship abnormal behavior identification based on the improved Hausdorff distance:
% 读取船舶数据
data = load('ship_data.mat');
ship_data = data.ship_data;
% 计算改进的Hausdorff