DBSCAN Algorithm Research Articles

Sustainable Spatial Features of Settlements along the Miao Frontier Wall and Miao Frontier Corridor Analyzed through Machine Learning Clustering

This study employed unsupervised machine learning clustering algorithms to systematically analyze the spatial layout characteristics of residential buildings in villages along the Miao Frontier Wall and Miao Frontier Corridor in Western Hunan. The results indicated significant differences between the two regions in terms of the number of building clusters, distribution patterns, and compactness. A comparative analysis of the K-means and DBSCAN algorithms revealed that K-means is more effective in uncovering the internal spatial layout characteristics of settlements. Further analysis showed that villages along the Miao Frontier Wall exhibited greater diversity and complexity, whereas those along the Miao Frontier Corridor demonstrated higher clustering efficiency and denser internal building distribution. These differences can be attributed to variations in historical functions, geographical environments, planning concepts, and social structures. This research uncovers the spatial layout patterns of traditional settlements and proposes a machine learning-based approach to cultural heritage preservation, providing a theoretical foundation for future heritage conservation and spatial optimization, thereby promoting the sustainable development and protection of traditional cultural heritage.

Automatic High-Resolution Operational Modal Identification of Thin-Walled Structures Supported by High-Frequency Optical Dynamic Measurements.

High-frequency optical dynamic measurement can realize multiple measurement points covering the whole surface of the thin-walled structure, which is very useful for obtaining high-resolution spatial information for damage localization. However, the noise and low calculation efficiency seriously hinder its application to real-time, online structural health monitoring. To this end, this paper proposes a novel high-resolution frequency domain decomposition (HRFDD) modal identification method, combining an optical system with an accelerometer for measuring high-accuracy vibration response and introducing a clustering algorithm for automated identification to improve efficiency. The experiments on the cantilever aluminum plate were carried out to evaluate the effectiveness of the proposed approach. Natural frequency and damping ratios were obtained by the least-squares complex frequency domain (LSCF) method to process the acceleration responses; the high-resolution mode shapes were acquired by the singular value decomposition (SVD) processing of global displacement data collected by high-speed cameras. Finally, the complete set of the first nine order modal parameters for the plate within the frequency range of 0 to 500 Hz has been determined, which is closely consistent with the results obtained from both experimental modal analysis and finite element analysis; the modal parameters could be automatically picked up by the DBSCAN algorithm. It provides an effective method for applying optical dynamic technology to real-time, online structural health monitoring, especially for obtaining high-resolution mode shapes.

Efficient detection of data entry errors in large-scale public health surveys: an unsupervised machine learning approach

Data entry errors in large-scale public health surveys can undermine the effectiveness of data-driven interventions. Therefore, identifying these data entry errors is crucial for public health experts. In large-scale public health surveys, manually verifying the accuracy of every data point by domain experts is nearly impossible. This study evaluates unsupervised machine learning algorithms for detecting these errors, focusing on the 'weight' parameter in the Annual Health Survey (AHS) dataset. The AHS, conducted by the Ministry of Health and Family Welfare, Government of India, in collaboration with the Registrar General of India, is a large-scale, stratified, household-level survey targeting maternal and child health across nine states in India. The dataset is freely available on the Open Government Data (OGD) Platform of India for public health research. In this study, five algorithms—DBSCAN, K-Means, Gaussian Mixture Model (GMM), Isolation Forest (IF), and One-Class SVM (1C-SVM) were applied to detect erroneous data entries. The evaluation process involved comprehensive preprocessing and feature engineering to optimize detection capabilities. Performance metrics such as precision, recall, accuracy, false anomaly, and missed anomaly rates were used to assess each algorithm. Among these, DBSCAN demonstrated superior performance, achieving a recall of 94.7% and a precision of 81.9%, making it highly effective for this task. The findings underscore the potential of unsupervised machine learning in automating the detection of data entry errors, thereby improving the integrity of public health data. This research contributes to the advancement of precision public health, supporting more accurate and reliable evidence-based decision-making and policy formulation.

ICESat-2 single photon laser point cloud denoising algorithm based on improved DBSCAN clustering

AbstractThe Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) has great potential for development due to its advantages of the use of multiple beams, low energy consumption, high repetition frequency, and high measurement sensitivity. However, the weak photon signal emitted by the photon counting lidar is susceptible to the background noise caused by the sun and the atmosphere, which can seriously affect the processing and application of laser data. This paper proposes an improved DBSCAN clustering algorithm for denoising single photon laser point clouds in mountainous areas. Firstly, a grouping method based on elevation and distance statistics is proposed to reduce the influence of terrain undulations on denoising accuracy. Finally, an automatic radius search method is put forward to determine clustering radius of each group, automatically find the optimal radius, and improve the existing DBSCAN clustering method. The method proposed in this paper is compared with the classical DBSCAN algorithm. The results show that the proposed algorithm significantly improves denoising accuracy in mountainous areas and effectively filters out most background noise. Graphical Abstract

Comprehensive Study on Optimizing Inland Waterway Vessel Routes Using AIS Data

Inland waterway transport is an important mode of transportation for many countries and regions. Route planning optimization can reduce navigation time, avoid traffic congestion, and improve transportation efficiency. In actual operations, many vessels determine their navigation routes based on the experience of their shipowners. When the captain fails to obtain accurate information, experience-based routes may pose significant navigation risks and may not consider the overall economic efficiency. This study proposes a comprehensive method for optimizing inland waterway vessel routes using automatic identification system (AIS) data, considering the geographical characteristics of inland waterways and navigation constraints. First, AIS data from vessels in inland waters are collected, and the multi-objective Peak Douglas–Peucker (MPDP) algorithm is applied to compress the trajectory data. Compared to the traditional DP algorithm, the MPDP algorithm reduces the average compression rate by 5.27%, decreases length loss by 0.04%, optimizes Euclidean distance by 50.16%, and improves the mean deviations in heading and speed by 23.53% and 10.86%, respectively. Next, the Ordering Points to Identify the Clustering Structure (OPTICS) algorithm is used to perform cluster analysis on the compressed route points. Compared to the traditional DBSCAN algorithm, the OPTICS algorithm identifies more clusters that are both detailed and hierarchically structured, including some critical waypoints that DBSCAN may overlook. Based on the clustering results, the A* algorithm is used to determine the connectivity between clusters. Finally, the nondominated sorting genetic algorithm II is used to select suitable route points within the connected clusters, optimizing objectives, including path length and route congestion, to form an optimized complete route. Experiments using vessel data from the waters near Shuangshan Island indicate that, when compared to three classic original routes, the proposed method achieves path length optimizations of 4.28%, 1.67%, and 0.24%, respectively, and reduces congestion by 24.15%. These improvements significantly enhance the planning efficiency of inland waterway vessel routes. These findings provide a scientific basis and technical support for inland waterway transport.

Research on differentiated lightning protection application of power lines based on laser point cloud data

Abstract The collection and processing of data in lightning protection assessment of transmission lines takes a long time and accuracy is difficult to ensure. Therefore, a laser point cloud data registration method based on pole and tower material coordinate transformation is proposed for differential lightning protection evaluation of transmission lines. Using DBSCAN algorithm, combined with point cloud data voxel filtering and noise reduction methods, to achieve data segmentation of transmission line tower body. Based on the characteristics of the symmetrical structure of the tower body, the coordinate transformation method is adopted to achieve the registration of multiple point cloud data. The parameters in the tower structure are obtained by concatenating and fusing elements such as the wire point cloud, lightning protection line point cloud, and tower point cloud. After verification by lightning protection assessment, the proposed method simplifies the processing flow of data compliance detection during the assessment process, reduces the time cost of lightning protection assessment, and greatly improves the accuracy and effectiveness of lightning risk assessment results.

Application of Improved HF-DBSCAN Algorithm in Analyzing Complex Trajectory Data of Wi-Fi Users in Smart Campus

ABSTRACT With the development of wireless network technology and the increasingly widespread application of mobile intelligent terminals, a large amount of wireless network data has been generated in smart campuses. By analyzing the trajectory data through clustering algorithms, more valuable user data information can be obtained. However, current clustering algorithms have high computational difficulty, long running time, and poor clustering accuracy for complex trajectories. Based on this background, the study proposes a spatial clustering of Application with Noise by Density-Based Spatial Clustering of Application with Noise combining Hausdorff and Frechet. The method is denoted as (Hausdorff and Frechet-Density Based Spatial Clustering of Application with Noise, HF-DBSCAN). DBSCAN denotes the Density Based Spatial Clustering of Application with Noise. In the experimental results, compared to the AH-DBSCAN, DBSCAN, and FD-DBSCAN algorithms, the running time of HF-DBSCAN algorithm in complex trajectory cluster analysis is reduced by 59.33%, 39.82%, and 35.12%, respectively, and the contour coefficient is closer to 1; the Davies-Boldin Index (DBI) values decreased by 59.24%, 64.42%, and 68.24%, respectively. The experiment shows that the optimized HF-DBSCAN algorithm has lower computational difficulty, better clustering performance, and higher clustering accuracy, which verifies the effectiveness of this study.

Drone Detection Using Dynamic-DBSCAN and Deep Learning in an Indoor Environment

Drones have found extensive utility in both public and personal places. Consequently, the accurate detection and tracking of drones have emerged as pivotal endeavors in terms of ensuring their optimal performance. This research paper introduces a novel application for discerning the movements of humans and drones from cloud points through the utilization of frequency-modulated continuous wave radar. The dynamic density-based spatial clustering of applications with noise (Dynamic-DBSCAN) algorithm was employed to classify cloud points into separate groups corresponding to the number of objects within the tracking area. Compared to the original DBSCAN algorithm, this method increased accuracy by about 16.8%, achieving an accuracy of up to 93.99%. Subsequently, a trio of deep learning algorithms— long short-term memory, deep neural network, and residual network (ResNet)—were harnessed to ascertain the categorization of each group as either human or drone. According to the results, ResNet achieved the best accuracy rate of 97.72%. Overall, this study underscores the efficacy of the proposed method in accurately and efficiently distinguishing between human and drone entities for effective monitoring and management.

Detection of Random Body Movements Using Clustering-Based Methods in Bioradar Systems

Bioradar systems, in general, refer to radar systems used for the detection of vital signs. These systems hold significant importance across various sectors, particularly in healthcare and surveillance, due to their capacity to provide contactless solutions for monitoring physiological functions. In these applications, the primary challenge lies in the presence of random body movements (BMs), which can significantly hinder the accurate detection of vital signs. To compensate the affected signal in a timely manner, portions of BM must be correctly identified. To address this challenge, this work proposes a solution based on the Density-Based Spatial Clustering of Applications with Noise (DBScan) algorithm to detect the occurrence of BM in radar signals. The main idea of this algorithm is to cluster the radar samples, aiming to differentiate between segments in which the subject is stable and segments in which the subject is moving. Using a dataset involving eight subjects, the proposed method successfully detects three types of body movements: chest movement, body rotation, and arm movement. The achieved results are promising, with F1 scores of 0.83, 0.73, and 0.8, respectively, for the detection of each specific movement type.

Binning Metagenomic Contigs Using Contig Embedding and Decomposed Tetranucleotide Frequency.

Metagenomic binning is a crucial step in metagenomic research. It can aggregate the genome sequences belonging to the same microbial species into independent bins. Most existing methods ignore the semantic information of contigs and lack effective processing of tetranucleotide frequency, resulting in insufficient and complex feature information extracted for binning and poor binning results. To address the above problems, we propose CedtBin, a metagenomic binning method based on contig embedding and decomposed tetranucleotide frequency. First, the improved BERT model is used to learn the contigs to obtain their embedding representation. Secondly, the tetranucleotide frequencies are decomposed using a non-negative matrix factorization (NMF) algorithm. After that, the two features are spliced and input into the clustering algorithm for binning. Considering the sensitivity of the DBSCAN clustering algorithm to input parameters, in order to solve the drawbacks of manual parameter input, we also propose an Annoy-DBSCAN algorithm that can adaptively determine the parameters of the DBSCAN algorithm. This algorithm uses Approximate Nearest Neighbors Oh Yeah (Annoy) and combines it with a grid search strategy to find the optimal parameters of the DBSCAN algorithm. On simulated and real datasets, CedtBin achieves better binning results than mainstream methods and can reconstruct more genomes, indicating that the proposed method is effective.

Enhancing International Trade Security: Real-Time Risk Assessment in Brazilian Customs with Blockchain Technology

This study introduces an innovative approach by integrating blockchain technology with risk analysis methods for international trade. The practical application and tangible improvements observed in a real case study highlight the originality and value of this approach, offering a robust framework for future research and practical implementation. The security of exports presents a challenge for customs authorities, requiring the analysis of vast volumes of data from diverse sources and stakeholders. Blockchain technology offers a viable solution by facilitating the sharing of reliable and secure data among participants in the international supply chain. This study proposes a distributed risk analysis method based on Blockchain to detect and prevent fraud and irregularities in Brazilian exports. The technique aims to enhance efficiency and protection in customs clearance, providing real-time risk analysis feedback at various supply chain stages. The method includes four stages: data collection, risk classification, validation, and feedback. It employs a Model Form for Insertion into the Blockchain, the DBSCAN algorithm to identify out-of-pattern transactions (outliers) at the manufacturing stage, and a Monte Carlo simulation to analyze the volume and specific products exported from Rio Grande do Sul. We applied this method in a case study of beef exports from Brazil. Using this method led to significant improvements in processing times, operational costs, transparency, and security in the supply chain. The blockchain-based approach identified fraudulent activities, such as value tampering and undeclared goods, enhancing compliance and trust among trade partners. Implementing this method can significantly reduce operational costs and processing times in international trade while increasing security and transparency. This approach benefits customs authorities and private operators, promoting a more reliable and efficient commercial environment. Adopting blockchain technology in international trade can reduce fraudulent and illicit practices, promoting greater fairness and equity in commercial transactions. Increased transparency strengthens trust among various actors in the supply chain, fostering a sense of social responsibility.

The Strategy of Human Resource Management Based on the K-Means Clustering Algorithm

With the continuous accumulation of enterprise data and the continuous development of technology, data-driven decision-making has become an important trend in enterprise management. Human resource management, as an important component of enterprise management, also requires the use of data analysis tools to optimize the decision-making process. Traditional human resource management strategy algorithms are often based on experience and intuition, lacking scientific data support. These algorithms often struggle to achieve ideal results when dealing with large and complex human resource data. This study aims to provide more scientific and accurate data support for human resource management by introducing the K-means algorithm. The main research purpose of this paper is to detect and identify the data in enterprise human resource management. Data mining can realize the comprehensive detection and analysis of poetry, but the application of data mining algorithms in human resource management data processing is not much. This paper hopes to analyze the anomaly of human resource data statistics by data mining method. According to the characteristics of the K-means algorithm and DBSCAN algorithm, a hybrid K-means algorithm is proposed in this study, which can automatically generate a more appropriate K value and realize parameter self-adaptation with less manual intervention. The optimized clustering K-means algorithm obtains the initial value of DBSCAN through calculation, and then the abnormal data of human resources statistics can be obtained through the second clustering calculation. The optimized K-means algorithm can detect human resource statistics data in real time, and give a new idea for human resource analysis of data anomalies.

Species discrimination of β-phenylethylamine, NaCl and NaOH based on Ultraviolet spectroscopy and principal component analysis combined with improved clustering by fast search and find of density peaks algorithm

This paper aimed at putting forward an approach integrating the improved clustering by fast search and find of density peaks (I-CFSFDP) algorithm with Ultraviolet (UV) spectroscopy for identifying the species of NaCl, NaOH, β-phenylethylamine(PEA) and their mixtures. For solving the issue that the clustering precision of the CFSFDP algorithm relies on the density forecast of the dataset and the manually selection of the truncated distance dc. The idea of kernel density forecast was adopted to the I-CFSFDP algorithm. The I-CFSFDP algorithm can observe the clusters of arbitrary shapes and use an adaptive method to evaluate the truncated distance dc, thereby generating more accurate clusters and identifying the core points in the clusters effectively. The dimensions of the UV spectra was reduced with principal component analysis (PCA), and the results of PCA were invoked as the input of the I-CFSFDP algorithm. Meanwhile, the effect of PCA-I-CFSFDP was evaluated by recall, accuracy, F-Score and precision. Besides, the DBSCAN and PCA-CFSFDP algorithms were used to compare with the PCA-I-CFSFDP algorithm. All of the classification outcomes displayed that the PCA-I-CFSFDP algorithm has better performance than the DBSCAN and PCA-CFSFDP algorithms. Therefore, the PCA-I-CFSFDP algorithm integrated with UV spectroscopy is a simple, quick and credible identification approach for detecting PEA, NaCl, NaOH and the mixtures.

Extraction of River Water Bodies Based on ICESat-2 Photon Classification

The accurate extraction of river water bodies is crucial for the utilization of water resources and understanding climate patterns. Compared with traditional methods of extracting rivers using remote sensing imagery, the launch of satellite-based photon-counting LiDAR (ICESat-2) provides a novel approach for river water body extraction. The use of ICESat-2 ATL03 photon data for inland river water body extraction is relatively underexplored and thus warrants investigation. To extract inland river water bodies accurately, this study proposes a method based on the spatial distribution of ATL03 photon data and the elevation variation characteristics of inland river water bodies. The proposed method first applies low-pass filtering to denoised photon data to mitigate the impact of high-frequency signals on data processing. Then, the elevation’s standard deviation of the low-pass-filtered data is calculated via a sliding window, and the photon data are classified on the basis of the standard deviation threshold obtained through Gaussian kernel density estimation. The results revealed that the average overall accuracy (OA) and Kappa coefficient (KC) for the extraction of inland river water bodies across the four study areas were 99.12% and 97.81%, respectively. Compared with the improved RANSAC algorithm and the combined RANSAC and DBSCAN algorithms, the average OA of the proposed method improved by 17.98% and 7.12%, respectively, and the average KC improved by 58.38% and 17.69%, respectively. This study provides a new method for extracting inland river water bodies.

Multi-Person Action Recognition Based on Millimeter-Wave Radar Point Cloud

Human action recognition has many application prospects in human-computer interactions, innovative furniture, healthcare, and other fields. The traditional human motion recognition methods have limitations in privacy protection, complex environments, and multi-person scenarios. Millimeter-wave radar has attracted attention due to its ultra-high resolution and all-weather operation. Many existing studies have discussed the application of millimeter-wave radar in single-person scenarios, but only some have addressed the problem of action recognition in multi-person scenarios. This paper uses a commercial millimeter-wave radar device for human action recognition in multi-person scenarios. In order to solve the problems of severe interference and complex target segmentation in multiplayer scenarios, we propose a filtering method based on millimeter-wave inter-frame differences to filter the collected human point cloud data. We then use the DBSCAN algorithm and the Hungarian algorithm to segment the target, and finally input the data into a neural network for classification. The classification accuracy of the system proposed in this paper reaches 92.2% in multi-person scenarios through experimental tests with the five actions we set.

Research on the Optimization of Ship Trajectory Clustering Based on the OD–Hausdorff Distance

With the growth of global trade, port shipping is becoming more and more important. In this paper, an analysis of a ship’s inbound and outbound track characteristics is conducted using the OD–Hausdorff distance. The accuracy and efficiency of trajectory data analysis have been enhanced through clustering analysis. Trajectories are arranged in a time sequence, and representative port segments are selected. An improved OD–Hausdorff distance method is employed to capture the dynamic characteristics of a ship’s movements, such as speed and heading. Additionally, the DBSCAN algorithm is utilized for clustering, allowing for the processing of multidimensional AIS data. Data cleaning and preprocessing have ensured the reliability of the AIS data, and the Douglas–Peucker algorithm is used for trajectory simplification. Significant improvements in the accuracy and efficiency of trajectory clustering have been observed. Therefore, the main channel of the Guan River and the right side of Yanwei Port are usually followed by ships greater than 60 m in length, with a lateral Relative Mean Deviation (RMD) of 7.06%. Vessels shorter than 60 m have been shown to have greater path variability, with a lateral RMD of 7.94%. Additionally, a crossing pattern at Xiangshui Port is exhibited by ships shorter than 60 m due to the extension of berths and their positions at turns. Enhanced clustering accuracy has provided more precise trajectory patterns, which aids in better channel management.

Construction of maritime traffic network based on DBSCAN

Abstract Trajectory data is essentially a sequence of spatial points ordered by timestamps, usually with some descriptive information in addition to basic spatiotemporal information. This paper investigates how publicly available Automatic Identification System (AIS) data can be used to analyze maritime traffic and transform it into directed graphs for estimating potential destination points of trajectories. In the maritime field, analyzing and modeling maritime traffic is crucial for vessel safety and efficiency, creating pathways with waypoints and segments, and further forming traffic networks. Our approach incorporates a detailed analysis of the distribution characteristics of different types of navigational points, leading to the adoption of tailored clustering parameters and methods. This differentiation allows for a nuanced understanding of stationary points at docks and anchorages, as well as navigational changes along shipping routes. Using the DBSCAN algorithm, we successfully cluster similar waypoints, considering cluster density and shape without needing to predefine the cluster count.

Monocular Vision Sensor-Based Indoor Road and Stair Detection

Road detection is an essential component of indoor robot navigation. Vision sensors have great advantages in road detection as they can provide rich information in terms of environmental perception. In this paper, a monocular vision sensor-based method for indoor road and stair detection is proposed, which detects feasible areas in indoor environments very fast without paying attention to detailed features of walls or other obstacles. More specifically, for a given indoor road image captured by an on-board vision sensor, the simple linear iterative clustering (SLIC) algorithm-based approach for efficient image segmentation is introduced. Then, according to the DBSCAN algorithm, the generated superpixels are clustered to form large areas of view. The initial road area is obtained through a safe window on the middle bottom of the image. In order to achieve a more accurate road segmentation, the initial image is processed by the binary search, edge detection based on the Canny operator and straight-line detection and location based on the Hough transform, which integrates edge and stair information into road detection. Several experiments are performed to evaluate the performance of the proposed method. The experimental results demonstrate that the proposed method could accurately detect road information and staircase information in images and succeeds in addressing the indoor road-detection problem.

An Optimal Denoising Method for Spaceborne Photon-Counting LiDAR Based on a Multiscale Quadtree

Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) has excellent potential for obtaining water depth information around islands and reefs. Combining the density-based spatial clustering of applications with noise algorithm (DBSCAN) and multiscale quadtree analysis, we propose a new photon-counting lidar denoising method to discard the large amount of noise in ICESat-2 data. First, the kernel density estimation (KDE) is used to preprocess the point cloud data, and a threshold is set to remove the noise photons on the sea surface. Next, the DBSCAN algorithm is used to preliminarily remove underwater noise photons. Then, the quadtree segmentation and Otsu algorithm are used for fine denoising to extract accurate bottom signal photons. Based on ICESat-2 pho-ton-counting data from six typical islands and reefs worldwide, the proposed method outperforms other algorithms in terms of denoising effect. Compared to in situ data, the determination coefficient (R2) reaches 94.59%, and the root mean square error (RMSE) is 1.01 m. The proposed method can extract accurate underwater terrain information, laying a foundation for offshore bathymetry.

Comparative Analysis Clustering Algorithm for Government’s Budget Performance Data

The government's budget performance is a benchmark for the government's success in optimizing people's money to achieve national goals. Even though performance measurement has reached the Work Unit level, the data formed still do not have a specific grouping, in the sense of unstructured data. The purpose of this research is to find the best clustering algorithm for classifying budget performance data. The data used is budget performance data for 19,460 Indonesian Government Work Units. The data is sourced from the SMART application and the OM SPAN application. This research uses a comparative study approach for the K-Means algorithm, DBSCAN, and agglomerative hierarchical clustering (AHC). Evaluation of the clustering results formed using the Davies-Bouldin Index (DBI) method. The AHC algorithm with k = 6 achieved the lowest DBI value of 0.3583472. The DBI value for the DBSCAN algorithm with MinPts = 10 is 0.5398259. However, the AHC algorithm is not good in terms of ease of implementation. Therefore, the K-means algorithm with parameters k = 10 is the best alternative. The K-Means algorithm gets a DBI value of 1.052678. The K-Means algorithm produces 10 clusters. Based on knowledge extraction, it is determined that cluster 2 and cluster 5 are ideal clusters in terms of budget performance. While the clusters that require attention are cluster 1, cluster 3, cluster 4, and cluster 8.