Cluster Parameters Research Articles

Deep Reinforcement Learning and Enhanced Optimization for Real-Time Energy Management in Wireless Sensor Networks

Constraints are a major issue in radio-based communication in Wireless Sensor Networks, where each sensor node has a limited amount of power. Conventional clustering and optimization methods have been inappropriate for dynamic conditions which lead to timely energy drainage and reduce the network lifetime. In this research, the novel Deep Reinforcement Learning-Enhanced Hybrid African Vulture and Aquila Optimizer has been proposed that optimizes the dynamic clustering and energy-based parameters in real time. The proposed model is designed for optimizing the Wireless Sensor Networks, by including Deep Reinforcement Learning to adjust the dynamic formation of the base of the cluster on real-time data which leads to efficient energy utilization among all the sensor nodes. It combines the best properties of the Aquila and African Vulture Optimizer to optimize the network lifetime and energy consumption. The network lifetime, which is one of the most crucial characteristics, is optimized by using the global search algorithm of African Vulture Optimiser. In contrast, it is optimized by the localized search of Aquila optimizer to reduce energy consumption. The presented novel African Vulture and Aquila model outperforms the existing methods used convention-based optimization methods. It shows a 20% improvement in energy efficiency and faster convergence with better robustness while keeping the network scalability. The proposed approach is perfectly suited for the scalable WSNs which are mainly used in the environment such as smart cities and IoT systems where a timely adaptation process is inevitable.

Dandelion center detection in perennial ryegrass with Heat maps using Convolutional Neural Networks

Industrial agricultural practices largely involve automated weed control processes and techniques. This phenomenon can also be seen in horticulture and landscaping. Generally, dandelion weeds (Taraxacum officinale) are a common pest and their detection is necessary for any type of removal. To address this detection issue, a cost-effective and intuitive labeling method using Heat maps is proposed for marking dandelion plant centers within perennial rye-grass. This method relies on approximate localization as opposed to pinpoint accuracy. An expandable lightweight Convolutional Neural Network (CNN) is built on a base network to generate detection output maps at two resolutions. Multiple loss functions are expanded to multi-instance predictions and their combinations are examined through ablation to assess and rank their performance. Different methods of computing standard performance metrics are also explored. Also, different backbone networks are also shown to reveal varying performance advantages. Through these methods, dandelion weed centers can consistently be located with robustness to noise and erroneous labels and with good precision. Furthermore, our method is almost entirely end-to-end. The experimental results demonstrate that our methods outperform Semantic Segmentation models in the precision of output maps while avoiding the need of intensive labeling costs. In addition, when applying Hierarchical Clustering to the segmentation maps for a complete comparison in center detection, our methods double the accuracy and do not require the manual tuning of cluster parameters. Our proposed application of soft computing can be used in the landscaping industry and adapted to other fields with relative ease. The binary classification and object detection tasks of locating dandelion plants can be extended to multi-class problems with other plants.

Response of Flame Seedless Grapevines to Foliar Spray with Magnesium Sulphate and Seaweed Extract

In order to preserve the health of vineyards, viticulture is heavily reliant on phytochemicals. Nevertheless, the development of eco-friendly strategies is being driven by green regulations in order to reduce their accumulation in the environment. In this regard, seaweeds have been demonstrated to be one of the marine resources with the greatest potential as plant protective agents, offering an environmentally benign alternative approach to sustainable grapevine production. The present study was conducted in vineyard located at West of the West Desert Road, Al-Qusiya city, Assiut Governorate to assessed the effectiveness of the solo or dual foliar application of magnesium sulphate and/or seaweed extract at various dosage (0.05, 0.1 and 0.2% for each one) on the yield, cluster parameters and berries physio-chemical quality of Flame Seedless grapevines throughout 2023 and 2024 seasons using randomized complete block design in ten treatments with 3 replicates. The results were summarized that applying seaweed extract was more efficacious than magnesium sulphate particularly at 0.2% followed by 0.1%. foliar spraying with 0.2% for both seaweed extract and magnesium sulphate was the most effective treatment on studied traits followed by the concentration of 0.1% for both without significant differences. Therefore, the combination of 0.1% magnesium sulphate + 0.1% seaweed extract has proven to be an effective approach for enhancing the growth, yield, and both physical and chemical quality of Flame seedless grapes in Egypt under sandy soil.

Robust and stochastic sparse subspace clustering

Sparse subspace clustering (SSC) has been widely employed in machine learning and pattern recognition, but it still faces scalability challenges when dealing with large-scale datasets. Recently, stochastic SSC (SSSC) has emerged as an effective solution by leveraging the dropout technique. However, SSSC cannot robustly handle noise, especially non-Gaussian noise, leading to unsatisfactory clustering performance. To address the above issues, we propose a novel robust and stochastic method called stochastic sparse subspace clustering with the Huber function (S3CH). The key idea is to introduce the Huber surrogate to measure the loss of the stochastic self-expression framework, thus S3CH inherits the advantage of the stochastic framework for large-scale problems while mitigating sensitivity to non-Gaussian noise. In algorithms, an efficient proximal alternating minimization (PAM)-based optimization scheme is developed. In theory, the convergence of the generated sequence is rigorously proved. Extensive numerical experiments on synthetic and six real datasets validate the advantages of the proposed method in clustering accuracy, noise robustness, parameter sensitivity, post-hoc analysis, and model stability.

Distributed PV carrying capacity prediction and assessment for differentiated scenarios based on CNN-GRU deep learning

The increasing penetration of distributed photovoltaic (PV) brings challenges to the safe and reliable operation of distribution networks, distributed PV access to the grid changes the characteristics of the traditional distribution grid. Therefore, the assessment of distributed PV carrying capacity is of great significance for distribution network planning. To this end, a differentiated scenario-based distributed PV carrying capacity assessment method based on a combination of Convolutional Neural Networks (CNN) and Gated Recurrent Unit (GRU) is proposed. Firstly, the meteorological characteristics affecting PV power are quantitatively analyzed using Pearson’s correlation coefficient, and the influence of external factors on PV power characteristics is assessed by integrating the measured data. Then, for the problem of high blindness of clustering parameters and initial clustering centers in the K-means clustering algorithm, the optimal number of clusters is determined by combining the cluster Density Based Index (DBI) and hierarchical clustering. The improved K-means clustering method reduces the complexity of massive scenarios to obtain distributed PV power under differentiated scenarios. On this basis, a distributed PV power prediction method based on the CNN-GRU model is proposed, which employs the CNN model for feature extraction of high-dimensional data, and then the temporal feature data are optimally trained by the GRU model. Based on the clustering results, the solution efficiency is effectively improved and the accurate prediction of distributed PV power is realized. Finally, taking into account the PV access demand of the distribution network, combined with the power flow calculation of distribution network, the bearing capacity of distribution network considering node voltage in differentiated scenarios is evaluated. In addition, verified by source-grid-load measured data in IEEE 33-bus distribution system. The simulation results show that the proposed CNN-GRU fusion deep learning model can accurately and efficiently assess the distributed PV carrying capacity of the distribution network and provide theoretical guidance for realizing distributed PV access on a large scale.

Enhanced permeability prediction in porous media using particle swarm optimization with multi-source integration

Accurately and efficiently predicting the permeability of porous media is essential for addressing a wide range of hydrogeological issues. However, the complexity of porous media often limits the effectiveness of individual prediction methods. This study introduces a novel Particle Swarm Optimization-based Permeability Integrated Prediction model (PSO-PIP), which incorporates a particle swarm optimization algorithm enhanced with dynamic clustering and adaptive parameter tuning (KGPSO). The model integrates multi-source data from the Lattice Boltzmann Method (LBM), Pore Network Modeling (PNM), and Finite Difference Method (FDM). By assigning optimal weight coefficients to the outputs of these methods, the model minimizes deviations from actual values and enhances permeability prediction performance. Initially, the computational performances of the LBM, PNM, and FDM are comparatively analyzed on datasets consisting of sphere packings and real rock samples. It is observed that these methods exhibit computational biases in certain permeability ranges. The PSO-PIP model is proposed to combine the strengths of each computational approach and mitigate their limitations. The PSO-PIP model consistently produces predictions that are highly congruent with actual permeability values across all prediction intervals, significantly enhancing prediction accuracy. The outcomes of this study provide a new tool and perspective for the comprehensive, rapid, and accurate prediction of permeability in porous media.

A Target Detection Algorithm Based on Fusing Radar with a Camera in the Presence of a Fluctuating Signal Intensity

Radar point clouds will experience variations in density, which may cause incorrect alerts during clustering. In turn, it will diminish the precision of the decision-level fusion method. To address this problem, a target detection algorithm based on fusing radar with a camera in the presence of a fluctuating signal intensity is proposed in this paper. It introduces a snow ablation optimizer (SAO) for solving the optimal parameters of the density-based spatial clustering of applications with noise (DBSCAN). Subsequently, the enhanced DBSCAN clusters radar point clouds, and the valid clusters are fused with monocular camera targets. The experimental results indicate that the suggested fusion method can attain a Balance-score ranging from 0.97 to 0.99, performing outstandingly in preventing missed detections and false alarms. Additionally, the fluctuation range of the Balance-score is within 0.02, indicating the algorithm has an excellent robustness.

Bayesian optimization of proton generation in terawatt laser–CH2 cluster interactions within a plasma channel

Improving the energy efficiency in generating high-energy proton or boron ions is crucial for advancing the feasibility of neutronless laser-based proton–boron (p-B11) fusion reactions. The primary objective of this work is to optimize the fusion energy efficiency of a proposed advanced p-B11 fusion scheme. In the proposed scheme, an ultrashort laser pulse is guided by a plasma channel filled with carbon–hydrogen (CH2) clusters. The MeV protons are generated by the Coulomb explosion (CE) of the cluster, which, therefore, interact with surrounding boron to produce alpha particles. To evaluate the fusion energy efficiency under various conditions, 2D particle-in-cell (PIC) simulations are used, supplemented with analytical calculations and estimations. The Bayesian optimization (BO) algorithm is utilized to optimize the key interaction parameters. The BO approach allows us to identify optimal cluster and laser parameters that would have higher fusion energy efficiency.

Enhanced Parameter Estimation of DENsity CLUstEring (DENCLUE) Using Differential Evolution

The task of finding natural groupings within a dataset exploiting proximity of samples is known as clustering, an unsupervised learning approach. Density-based clustering algorithms, which identify arbitrarily shaped clusters using spatial dimensions and neighbourhood aspects, are sensitive to the selection of parameters. For instance, DENsity CLUstEring (DENCLUE)—a density-based clustering algorithm—requires a trial-and-error approach to find suitable parameters for optimal clusters. Earlier attempts to automate the parameter estimation of DENCLUE have been highly dependent either on the choice of prior data distribution (which could vary across datasets) or by fixing one parameter (which might not be optimal) and learning other parameters. This article addresses this challenge by learning the parameters of DENCLUE through the differential evolution optimisation technique without prior data distribution assumptions. Experimental evaluation of the proposed approach demonstrated consistent performance across datasets (synthetic and real datasets) containing clusters of arbitrary shapes. The clustering performance was evaluated using clustering validation metrics (e.g., Silhouette Score, Davies–Bouldin Index and Adjusted Rand Index) as well as qualitative visual analysis when compared with other density-based clustering algorithms, such as DPC, which is based on weighted local density sequences and nearest neighbour assignments (DPCSA) and Variable KDE-based DENCLUE (VDENCLUE).

SparkDWM: a scalable design of a Data Washing Machine using Apache Spark.

Data volume has been one of the fast-growing assets of most real-world applications. This increases the rate of human errors such as duplication of records, misspellings, and erroneous transpositions, among other data quality issues. Entity Resolution is an ETL process that aims to resolve data inconsistencies by ensuring entities are referring to the same real-world objects. One of the main challenges of most traditional Entity Resolution systems is ensuring their scalability to meet the rising data needs. This research aims to refactor a working proof-of-concept entity resolution system called the Data Washing Machine to be highly scalable using Apache Spark distributed data processing framework. We solve the single-threaded design problem of the legacy Data Washing Machine by using PySpark's Resilient Distributed Dataset and improve the Data Washing Machine design to use intrinsic metadata information from references. We prove that our systems achieve the same results as the legacy Data Washing Machine using 18 synthetically generated datasets. We also test the scalability of our system using a variety of real-world benchmark ER datasets from a few thousand to millions. Our experimental results show that our proposed system performs better than a MapReduce-based Data Washing Machine. We also compared our system with Famer and concluded that our system can find more clusters when given optimal starting parameters for clustering.

Experimental study on hydraulic fracture propagation behavior in heterogeneous shale formations

The study of fracture propagation in heterogeneous shale is a crucial prerequisite for the investigation of heterogeneous cluster and perforation parameters optimization. In this paper, we conduct a physical simulation fracturing experiment on heterogeneous shale to investigate the effects of various influencing factors, such as shale bedding, near-wellbore fractures, lithological changes, and the presence of fractures surrounding the perforation hole, on fracture propagation law and morphology. Our research demonstrates that during shale fracturing, shear dislocation typically occurs between layers, resulting in the separation of different layer planes. The main fracture primarily propagates through layers in a stepped manner. The presence of sandstone in heterogeneous shale significantly impedes fracturing fractures, causing significant distortion and deviation. As the scale of natural fractures increases, it tends to cause the fracturing fracture to twist and change direction. The natural fractures network can also lead to the distortion of fracturing fractures, albeit to a lesser extent than large-scale natural fractures. The presence of micro fractures parallel to the perforation axis surrounding the perforation hole enhances the ability of the main fracturing fractures to pass through natural fractures.

Quantifying the heterogeneity impact of risk factors on regional bicycle crash frequency: A hybrid approach of clustering and random parameter model

The existence of internal and external heterogeneity has been established by numerous studies across various fields, including transportation and safety analysis. The findings from these studies underscore the complexity of crash data and the multifaceted nature of risk factors involved in accidents. However, most studies consider the effects of unobserved heterogeneity from one perspective −- either within clusters (internal) or between clusters (external) −- and do not investigate the biases from both simultaneously on crash frequency analysis. To fill this gap, this study proposes a hybrid approach combining latent class cluster analysis with the random parameter negative binomial regression model (LCA-RPNB) to explore the association between risk factors and bicycle crash frequency. First, the bicycle crash data is categorized into three clusters using LCA based on crash features such as gender, trip purposes, weather, and light conditions. Then, the separated crash frequency models for different clusters and the overall model are developed based on RPNB using regional factors of crash locations as independent variables and the crash frequency of different clusters respectively as dependent variables. The hybrid approach enables a comprehensive examination of internal and external heterogeneities among bicycle crash frequency factors simultaneously. Results suggest that the proposed hybrid approach exhibits superior fitting and predictive performance compared to the model only considers the effects of unobserved heterogeneity from one perspective with the lower values of Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). This approach can help policymakers and urban planners to design more effective safety interventions by understanding the distinct needs of different bicyclist clusters and the specific factors that contribute to crash risk in each group.

Hybrid Text Embedding and Evolutionary Algorithm Approach for Topic Clustering in Online Discussion Forums

Leveraging discussion forums as a medium for information exchange has led to a surge in data, making topic clustering in these platforms essential for understanding user interests, preferences, and concerns. This study introduces an innovative methodology for topic clustering by combining text embedding techniques—Latent Dirichlet Allocation (LDA) and BERT—trained on a singular autoencoder. Additionally, it proposes an amalgamation of K-Means and Genetic Algorithms for clustering topics within triadic discussion forum threads. The proposed technique begins with a preprocessing stage to clean and tokenize textual data, which is then transformed into a vector representation using the hybrid text embedding method. Subsequently, the K-Means algorithm clusters these vectorized data points, and Genetic Algorithms optimize the parameters of the K-Means clustering. We assess the efficacy of our approach by computing cosine similarities between topics and comparing performance against coherence and graph visualization. The results confirm that the hybrid text embedding methodology, coupled with evolutionary algorithms, enhances the quality of topic clustering across various discussion forum themes. This investigation contributes significantly to the development of effective methods for clustering discussion forums, with potential applications in diverse domains, including social media analysis, online education, and customer response analysis.

Machine learning identifies clusters of the normal adolescent spine based on sagittal balance.

This study applied a machine learning semi-supervised clustering approach to radiographs of adolescent sagittal spines from a single pediatric institution to identify patterns of sagittal alignment in the normal adolescent spine. We sought to explore the inherent variability found in adolescent sagittal alignment using machine learning to remove bias and determine whether clusters of sagittal alignment exist. Multiple semi-supervised machine learning clustering algorithms were applied to 111 normal adolescent sagittal spines. Sagittal parameters for resultant clusters were determined. Machine learning analysis found that the spines did cluster into distinct groups with an optimal number of clusters ranging from 3 to 5. We performed an analysis on both 3 and 5-cluster groups. The 3-cluster groups analysis found good consistency between methods with 96 of 111, while the analysis of 5-cluster groups found consistency with 105 of 111 spines. When assessing for differences in sagittal parameters between the groups for both analyses, there were differences in T4-12 TK, L1-S1 LL, SS, SVA, PI-LL mismatch, and TPA. However, the only parameter that was statistically different for all groups was SVA. Based on machine learning, the adolescent sagittal spine alignments do cluster into distinct groups. While there were distinguishing features with TK and LL, the most important parameter distinguishing these groups was SVA. Further studies may help to understand these findings in relation to spinal deformities.

Bio-Inspired Energy-Efficient Cluster-Based Routing Protocol for the IoT in Disaster Scenarios.

The Internet of Things (IoT) is a promising technology for sensing and monitoring the environment to reduce disaster impact. Energy is one of the major concerns for IoT devices, as sensors used in IoT devices are battery-operated. Thus, it is important to reduce energy consumption, especially during data transmission in disaster-prone situations. Clustering-based communication helps reduce a node's energy decay during data transmission and enhances network lifetime. Many hybrid combination algorithms have been proposed for clustering and routing protocols to improve network lifetime in disaster scenarios. However, the performance of these protocols varies widely based on the underlying network configuration and the optimisation parameters considered. In this research, we used the clustering parameters most relevant to disaster scenarios, such as the node's residual energy, distance to sink, and network coverage. We then proposed the bio-inspired hybrid BOA-PSO algorithm, where the Butterfly Optimisation Algorithm (BOA) is used for clustering and Particle Swarm Optimisation (PSO) is used for the routing protocol. The performance of the proposed algorithm was compared with that of various benchmark protocols: LEACH, DEEC, PSO, PSO-GA, and PSO-HAS. Residual energy, network throughput, and network lifetime were considered performance metrics. The simulation results demonstrate that the proposed algorithm effectively conserves residual energy, achieving more than a 17% improvement for short-range scenarios and a 10% improvement for long-range scenarios. In terms of throughput, the proposed method delivers a 60% performance enhancement compared to LEACH, a 53% enhancement compared to DEEC, and a 37% enhancement compared to PSO. Additionally, the proposed method results in a 60% reduction in packet drops compared to LEACH and DEEC, and a 30% reduction compared to PSO. It increases network lifetime by 10-20% compared to the benchmark algorithms.

Performance Evaluation of Different Clustering Techniques and Parameters of Hybrid PSO- and GA-ANFIS on Optimization and Prediction of Biomethane Yield of Alkali-Pretreated Groundnut Shells

AbstractThe study focuses on optimizing biomethane yield in the anaerobic digestion of alkali-pretreated groundnut shells, involving varied input parameters. Biomethane optimization will improve the economy of the technology, which will assist in managing the environmental challenges of fossil fuel combustion. Traditional methods prove challenging, inaccurate, and uneconomical, necessitating efficient optimization models. This research hybridizes particle swarm optimization (PSO) and genetic algorithms (GA) with adaptive neuro-fuzzy inference system (ANFIS) models, assessing input parameters’ influence on biomethane yield through renowned performance metrics. Comparing the best model in the hybrid analysis, encompassing pretreatments A-E, the PSO-ANFIS (RMSE = 1.1719, MADE = 0.6525, MAE = 0.9314, Theil’s U = 0.1844, and SD = 0.7737) outperformed the GA-ANFIS (RMSE = 1.9338, MADE = 0.9318, MAE = 1.6557, Theil’s U = 0.2734, SD = 1.0598), using the same cluster radius of 0.50. Furthermore, compared to the GA-ANFIS model, the PSO-ANFIS model demonstrated significant improvements across various metrics: RMSE by 39.40%, MADE by 29.97%, MAE by 43.75%, Theil’s U by 32.56%, and SD by 27.00%. Results indicate that the PSO-ANFIS model outperforms the GA-ANFIS model, emphasizing the importance of suitable clustering algorithms and precise parameter adjustment for optimal performance in predicting biomethane yield from pretreated lignocellulose feedstocks. Graphical Abstract

Optimizing Product Matching in E-Commerce with DOC2VEC: Leveraging Hierarchical Clustering Parameters Based on Product Titles

Information technology is pivotal in increasing efficiency and effectiveness in online retail, particularly in product matching. This research delves into the challenges associated with product matching in the e-commerce sector, addressing issues related to the diversity and ambiguity of product titles and the fast-paced introduction of new products to the market. As a solution, we implement a neural network-based approach. The main contribution of this research is the implementation and validation of the Doc2Vec method in the context of product matching for e-commerce products. Additionally, this study successfully identifies the optimal parameter combinations for Hierarchical Clustering, which has been tested and validated on 4,000 product title data points. The data for learning and evaluation comes from an online retail platform and includes 34,000 product names from various sectors. The research compares two Doc2Vec architectures for feature extraction from product titles and then integrates them with a Hierarchical Clustering approach to group similar products. The results indicate that the Doc2Vec model with the DBOW (Distributed Bag of Words) architecture yields a better average NMI (Normalized Mutual Information) Score than the DM (Distributed Memory) architecture.

Deriving physical parameters of unresolved star clusters

Context.Recently, it has been noticed that the discrepancies in the integrated colour indices (CIs) between star clusters and models are mostly due to the projection of bright stars in the apertures. In order to reduce this problem, the method of adaptive aperture photometry has been proposed. This method has been applied to star clusters from theM 31PanchromaticHubbleAndromeda Treasury (PHAT) survey, and studies show that the adaptive aperture photometry performs better than the conventional approach.Aims.The aim of this study is to determine the best achievable limits on the accuracy and applicability of the aperture photometry method for studying star clusters in the local Universe.Methods.We computed a large network of artificial 3D star clusters spanning the parameter space of theM 31clusters. We then simulated images of these clusters by projecting each onto a 2D plane from 100 directions. Star cluster images were generated in six passbands to match the PHAT survey. To investigate the limiting accuracy of aperture photometry and the limits of its applicability to star cluster studies, we measured the simulated images and performed parameter determination tests.Results.We demonstrate that star clusters with and without post-main-sequence stars have significant photometric differences. We show that in order to obtain reliable physical parameters of star clusters, the CIs must be measured using an aperture with a radius larger than the cluster’s half-light radius. Furthermore, we demonstrate that the parameter determination of young clusters (∼10 Myr) is problematic regardless of the aperture size used. Therefore, it is advisable to determine the parameters of these clusters using colour-magnitude diagram fitting methods, when possible. We also show that the randomness of the viewing angle can lead to a CI uncertainty of up to 0.1 mag, depending on cluster parameters and aperture size.

Future scenario generation and reduction methods for solar photovoltaic electricity production analysis

Abstract A morphing-clustering-based method of generation and reduction future scenario for solar photovoltaic electricity production analysis is studied. Present historical annual weather data are transformed into future scenarios and timeframes by a morphing method with a general circulation model. The Copula methodology is applied to model the forthcoming decade’s annual meteorological data and generate abundant intra-day hourly scenario samples. The comparative analysis of the photovoltaic power output variations between future and present in reduction typical scenarios is conducted by the K-means clustering method with appropriate clustering parameter optimization. The simulation results show that the annual mean variations in global horizontal radiation, dry bulb temperature, and wind speed are projected to increase by approximately from 20.1 to 36.5 W/m2, 1.6 to 2.3°C, and 0.1 to 0.6 m/s, respectively in the forthcoming scenario of shared socioeconomic pathways 2-4.5. It is seen that solar photovoltaic electricity production anticipates a positive increase from 2.5% to 17.9%.

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.