•Automatic tools for touristic applications are highly valuable for the user•Single-scale clustering methods are insufficient to solve real clustering problems•Multi-scale (hierarchical) ...density-based clustering improves landmark detection•The separation of inhabited population cores facilitates the clustering approach•Increasing the dimensionality improves the results within crowded sample spaces.
The process of determining relevant landmarks within a certain region is a challenging task, mainly due to its subjective nature. Many of the current lines of work include the use of density-based clustering algorithms as the base tool for such a task, as they permit the generation of clusters of different shapes and sizes. However, there are still important challenges, such as the variability in scale and density. In this paper, we present two novel density-based clustering algorithms that can be applied to solve this: K-DBSCAN, a clustering algorithm based on Gaussian Kernels used to detect individual inhabited cores within regions; and V-DBSCAN, a hierarchical algorithm suitable for sample spaces with variable density, which is used to attempt the discovery of relevant landmarks in cities or regions. The obtained results are outstanding, since the system properly identifies most of the main touristic attractions within a certain region under analysis. A comparison with respect to the state-of-the-art show that the presented method clearly outperforms the current methods devoted to solve this problem.
Clustering analysis is applied extensively in pattern recognition. In marine traffic applications, the clustering results may exhibit a customary route and traffic volume distribution. In order to ...improve the clustering performance of ship trajectory data, which is characterized by a large data volume and distribution complexity, a method consisting of Douglas-Peucker (DP)-based compression and density-based clustering is proposed. In the first part of the proposed method, the appropriate parameters for the DP algorithm were determined according to the shape changes in the trajectories, which were used to compress the trajectories prior to calculating the dynamic time warping (DTW) distance matrix. In the second part, the density-based spatial clustering of applications with noise (DBSCAN) algorithm was improved in terms of determining the parameters. Based on the statistical characteristics of ship trajectory distribution, the appropriate DBSCAN parameters could be determined adaptively. Evaluation and comparison experiments were conducted based on massive real ship trajectories in the Chinese port of Beilun-Zhoushan. The results demonstrated that, compared to the traditional DTW distance, the proposed similarity measurement exhibits superior performance in terms of both time and quality. Furthermore, the results of the comparison experiment demonstrated that the improved DBSCAN outperforms two existing clustering methods in marine traffic c pattern recognition.
Extracting travel patterns from large-scaled vehicle trajectories is the key step to analyze urban travel characteristics, which can also provide effective strategies for urban traffic planning, ...construction, management and policy decision. In this study, we adopt the DBSCAN (Density-Based Spatial Clustering of Application with Noise) algorithm by fusing spatial, temporal and directional attributes extracting from vehicle trajectories Furthermore, LCS (Longest Common Sequences) is adopted to estimate spatial similarity, and two measurements are also designed to evaluate the temporal and directional similarity between trajectories. Accordingly, a statistical feature-based parameter optimization method is proposed in the clustering process to achieve reasonable clustering results. Finally, trajectory data collected from Harbin city, China are used to validate the effectiveness of clustering method. A comparison of clustering results considering different combination of attributes is conducted to further demonstrate the advantage of the proposed model.
•An improved DBSCAN algorithm is proposed by fusing multi-attributes.•Spatial, temporal and directional attributes are extracted from trajectories.•The LCS is adopted to estimate spatial similarity between trajectories.•A statistical feature-based parameter optimization method is proposed.•A comparison is conducted to demonstrate the effectiveness of the model.
Aiming at the problem that it is difficult to accurately quantify ship regional collision risk under multi-ship encounter situation in complex waters, this paper proposes a novel arena-based regional ...ship collision risk assessment method that combines density clustering and multiple influence factors in respect to ship arena in complex waters. Firstly, the DBSCAN is used to cluster the position of the ship, and obtain the spatial distribution of the clusters of encounter ships (CES). Then considering the influence factors of DCPA, TCPA and relative bearing (RB) from the perspective of ship arena, the calculation function of ship regional collision risk is established, the comprehensive ship regional collision risk calculation model considering multiple factors is synthesized. In order to verify the effectiveness of the proposed method, the method has been applied to the west coast of Sweden waters. We have also conducted comparative analysis with the existed study, the results show that the proposed regional collision risk assessment considering the additional impact factors of RB can more effectively and accurately identify the ships with high collision risk. This paper provides a theoretical basis for shore-based safety surveillance center for autonomous ships in complex waters.
•A novel arena-based regional ship collision risk assessment method is proposed.•The DBSCAN is used to obtain the spatial distribution of the clusters of encounter ships.•The factors of DCPA, TCPA and relative bearing are considered in the proposed method.•The proposed method can more accurately identify multi-ship collision risk under the influence of relative bearing.
Detecting and identifying manipulated portions within images poses a formidable challenge in research. Manipulated images, often created using image editing software such as Picasa or Photoshop, ...serve to obscure information and intentionally mislead viewers. Consequently, ensuring the authenticity of images becomes imperative prior to extracting meaningful data. One prevalent form of tampering is copy-move forgery, where objects are intentionally duplicated within an image using region of the same image. This study introduces a method for detecting copy-move forgery areas based on locating Scale-Invariant Feature Transform (SIFT) keypoints in images. The SIFT technique is employed for feature extraction, while feature descriptors are matched using brute force matching. Subsequently, a clustering algorithm is applied to group spatially proximate keypoints, enabling the detection of cloned regions. Specifically, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is utilized to identify forged areas within the image. To mitigate erroneous forgery detection and reduce false positives, outlier detection techniques are employed. Experimental evaluations are conducted on the MICC-F220 and MICC-F600 datasets, with comparisons drawn against previously established methodologies.
This study explores the variability in stay point detection accuracy influenced by GPS log intervals and detection algorithms and confirms the impact of stay point detection on stay duration and trip ...frequency estimations. We compare three major detection algorithms across varied log intervals adjusted through down-sampling using five evaluation indices newly proposed in this study. Using GPS trajectory data with ground truth data collected in Hiroshima, Japan, we found that ST-DBSCAN, a time-distance density clustering method, offers the highest accuracy and maintains its performance up to a 5-minute interval. We also found that widely used conventional methods, including duration and distance-based methods and DBSCAN, would produce considerably biased results.
Machine learning has emerged as a powerful tool for both engineering and geo-localization applications. In this study, we investigate the Terabit/sec bandwidth wireless technology application using ...specialized ns-3 simulation tools. Through extensive simulations, we explore various scenarios with diverse parameters, including population density, topology types, and overlapping ratios among consecutive radio sectors centered around a single access point. To extract meaningful insights from the data, we employ the DBScan unsupervised learning method, enabling us to identify the optimal number of classes for sector efficiency features. Our optimization approach considers both the number of outliers and the minimum number of elements within each radio sector. By analysing a synthetic dataset generated from the simulation cases, we uncover valuable insights and establish the optimal working point for the system.
With the increase in the amount of data captured during the manufacturing process, monitoring systems are becoming important factors in decision making for management. Current technologies such as ...Internet of Things (IoT)-based sensors can be considered a solution to provide efficient monitoring of the manufacturing process. In this study, a real-time monitoring system that utilizes IoT-based sensors, big data processing, and a hybrid prediction model is proposed. Firstly, an IoT-based sensor that collects temperature, humidity, accelerometer, and gyroscope data was developed. The characteristics of IoT-generated sensor data from the manufacturing process are: real-time, large amounts, and unstructured type. The proposed big data processing platform utilizes Apache Kafka as a message queue, Apache Storm as a real-time processing engine and MongoDB to store the sensor data from the manufacturing process. Secondly, for the proposed hybrid prediction model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection and Random Forest classification were used to remove outlier sensor data and provide fault detection during the manufacturing process, respectively. The proposed model was evaluated and tested at an automotive manufacturing assembly line in Korea. The results showed that IoT-based sensors and the proposed big data processing system are sufficiently efficient to monitor the manufacturing process. Furthermore, the proposed hybrid prediction model has better fault prediction accuracy than other models given the sensor data as input. The proposed system is expected to support management by improving decision-making and will help prevent unexpected losses caused by faults during the manufacturing process.
Density peak (DP) and density-based spatial clustering of applications with noise (DBSCAN) are the representative clustering algorithms on the basis of density in unsupervised learning. They are ...capable of clustering data of arbitrary shape as well as identifying noise samples in a potential data set. Notwithstanding, DP algorithm depends on the decision graph when selecting the centers, it is difficult for users without priori knowledge to automatically as well as accurately identify cluster centers. The clustering performance exhibited by DBSCAN algorithm presents a strong sensitivity to parameter setting regarding Eps and MinPts. For dealing with afore-mentioned issues, we propose a new two-stage clustering method based on improved DBSCAN and DP algorithm (TSCM), which first use an improved DBSCAN algorithm based on bat optimization to generate initial clusters. Specifically, the improved DBSCAN takes a well-known internal clustering validation index without labels called Silhouette as fitness function to control the process of parameters determination by bat optimization. The cluster centers in decision graph are automatically selected according to the initial clusters. The final clusters are obtained by DP with the determined cluster centers. As found in the experiments, relative to DP and DBSCAN, TSCM can effectively overcome the manual intervention of cluster center selection in DP and parameters setting in DBSCAN. The clustering performance is significantly improved.
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