Damage Localization of Stacker’s Track Based on EEMD-EMD and DBSCAN Cluster Algorithms

A method of two-stage clustering learning based on improved DBSCAN and density peak algorithm

The problem of anomaly detection in marine Argo data is studied. Based on the common and widely used DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm for anomaly detection in Argo data, as in other fields, there is a major problem in the application of DBSCAN algorithm, which is how to choose the appropriate parameter pairs. To solve this problem, this paper proposes an improved version of DBSCAN, namely CCMD-DBSCAN (DBSCAN based on the classification characteristics of marine data), which solves the above problem by studying the characteristics and laws of Argo data and is successfully applied to anomaly detection of marine data. The experimental results show that the new algorithm can not only determine the appropriate parameter pairs but also has a good anomaly detection effect.KeywordsArgoDBSCANAnomaly detection

The density based notion for clustering approach is used widely due to its easy implementation and ability to detect arbitrary shaped clusters in the presence of noisy data points without requiring prior knowledge of the number of clusters to be identified. Density-based spatial clustering of applications with noise (DBSCAN) is the first algorithm proposed in the literature that uses density based notion for cluster detection. Since most of the real data set, today contains feature space of adjacent nested clusters, clearly DBSCAN is not suitable to detect variable adjacent density clusters due to the use of global density parameter neighborhood radius Nrad and minimum number of points in neighborhood Npts. So the efficiency of DBSCAN depends on these initial parameter settings, for DBSCAN to work properly, the neighborhood radius must be less than the distance between two clusters otherwise algorithm merges two clusters and detects them as a single cluster. Through this paper: 1) We have proposed improved version of DBSCAN algorithm to detect clusters of varying density adjacent clusters by using the concept of neighborhood difference and using the notion of density based approach without introducing much additional computational complexity to original DBSCAN algorithm. 2) We validated our experimental results using one of our authors recently proposed space density indexing (SDI) internal cluster measure to demonstrate the quality of proposed clustering method. Also our experimental results suggested that proposed method is effective in detecting variable density adjacent nested clusters.

In recent years, the clustering of multi-density data has been a research hotspot. As a widely applied clustering algorithm, density-based spatial clustering of application with noise (DBSCAN) algorithm can avoid the interference of noise data, and has the ability to find clusters of any shape. However, the DBSCAN algorithm used hyperparameter (e.g., eps, MinPts), which makes the clusters of different densities cannot be identified. Furthermore, when the algorithm is applied to different datasets, the clustering quality would decrease severely. To cope with the above problems, we propose an improved and heuristic-based iterative DBSCAN clustering algorithm. The new algorithm realizes flexible clustering of the data with different densities by MinPts. In particular, we first estimate the values of MinPts according to the user area densities, then the new clustering is carried out based on the MinPts. This method avoids too dense clusters. In the experiments, Silhouette Coefficient and purity are adopted to evaluate the clustering effect of the improved DBSCAN algorithm. It is demonstrated that the proposed algorithm can effectively cluster multi-density data, and has greater adaptability and clustering performance to all kinds of data.

Clustering algorithm is one of the most important algorithms in unsupervised learning. For density‐based spatial clustering of applications with noise (DBSCAN) density clustering algorithm, the selection of neighborhood radius and minimum number is the key to get the best clustering results. Aiming at the problems of traditional DBSCAN algorithm, such as the neighborhood radius and the minimum number of points, this article puts forward two classifications based on K‐means algorithm, and gets two clustering centers. Where calculated between two data points and the cluster center‐to ‐center distance, clustering, distance, statistics in a distance of data points within the scope of the search, the number of data points corresponding to the maximum distance value, and thus the parameters for the DBSCAN algorithm to estimate and selection of initial radius of neighborhood with the minimum number of clustering start critical value. When the parameters are iterated and optimized continuously, the data are divided into clusters, and the most suitable neighborhood radius and the minimum point number are obtained. The experimental data analysis show that the improved algorithm reduces the human factors in the traditional algorithm and improves the efficiency, so as to get the accurate clustering results.

In data-intensive environment, data mining and data analysis is a big challenge. It analyses basic algorithms, such as k-means algorithm and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). And both algorithms are implemented on MapReduce of Hadoop. Further, an improved Data-intensive Density Base Distributed Clustering (IDBDC) algorithm is proposed which are based on k-means algorithm and DBSCAN algorithm. And IDBDC algorithm is implemented on MapReduce of Hadoop as well. The experimental results show that the improved IDBDC algorithm has better performance.

In fingerprint-based localization systems, clustering fingerprint databases is a proposed technique for improving localization accuracy while reducing localization time. Among various clustering algorithms, density-based spatial clustering of applications with noise (DBSCAN) stands out for its robustness to outliers and ability to accommodate fingerprint databases of various shapes. However, the clustering performance of the DBSCAN algorithm is heavily influenced by the type of similarity measure metric used, with most researchers using distance-based metrics. This paper aims to enhance DBSCAN clustering by using a pattern-based metric known as the context similarity coefficient (CSC) instead of distance-based metrics. The CSC metric examines received signal strength (RSS) measurement patterns that form fingerprint vectors and assesses both linear and non-linear relationships between these vectors to determine similarity. Four publicly available fingerprint databases were used to evaluate the clustering performance with silhouette scores as a performance metric. The performance of the DBSCAN algorithm with the CSC metric is determined and compared to Euclidean and Manhattan distances as similarity measure metrics. Simulation results indicate that achieving good clustering performance with the DBSCAN algorithm requires generating three or fewer clusters. The proposed CSC metric demonstrated the best clustering performance in two of four fingerprint databases and the second-best in another. However, computational complexity comparisons reveal that the CSC metric is highly computationally intensive and is suggested to be used on small to medium-sized fingerprint databases generated using an odd number of wireless APs deployed in a non-uniform or non-grid-like distribution.

Revised DBSCAN algorithm to cluster data with dense adjacent clusters

Density-Based Spatial Clustering of Application with Noise (DBSCAN) is a well-known, unsupervised machine learning tool for clustering. DBSCAN creates clusters based on dense regions while marking points that lie alone in low-density regions as outliers. In DBSCAN, the density is detected via core points which are quite sensitive to input parameters: ϵ is radius of the neighborhood and MinPts is minimum number of points constraint within ϵ radius. In contrast to crisp core point definition, intuitionistic fuzzy core point definition makes DBSCAN algorithm capable to detect different patterns of density by two different combinations of input parameters. In this study, a DBSCAN extension is proposed based on this idea: IFDBSCAN. The proposed extension is then illustrated by a computational experiment on a synthetic dataset. Results show that, IFDBSCAN can establish fine-tuned clusters relatively to classical DBSCAN and provide users more insight on the estimation of input parameters.

In order to better mine information from the data of automatic identification system (AIS) and scientifically perceive the water traffic situation, several common clustering algorithms, such as K-means algorithm and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, are studied and analyzed, and the advantages and disadvantages of each algorithm and applicable scenarios are compared. Taking the actual ship's AIS trajectory data as an example, each algorithm is used to verify its characteristics, which provides convenience for further information mining of trajectory data.

An enhanced principal component analysis method with Savitzky–Golay filter and clustering algorithm for sensor fault detection and diagnosis

An efficient and scalable density-based clustering algorithm for datasets with complex structures

The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) is equipped with a 532-nm single-photon light detection and ranging (LiDAR), offering robust coastal bathymetric capabilities. However, extracting underwater topography remains challenging due to noise interference and the attenuation of photon density with increasing depth. To address these issues, this study introduces an elliptical neighborhood clustering algorithm with adaptive size and direction that enhances the conventional density-based spatial clustering of applications with noise (DBSCAN). Instead of using a circular neighborhood, we develop an elliptical neighborhood that adapts size and direction based on local seafloor features and depth variations. The algorithm adjusts the elliptical size in response to seafloor terrain changes, enhancing the accuracy of seafloor topography measurements in the coastal surrounding remote islands. This method is applied to process ICESat-2 single-photon point cloud data from the nearshore shallow regions of the Yuanzhi Island (South China Sea) and the Hawaiian Islands (Pacific Ocean). Validation against airborne LiDAR bathymetric data confirms the proposed method’s accuracy in both daytime and nighttime conditions, outperforming DBSCAN and directional ellipse algorithms. For nighttime data from Oahu Island, the method detects depths up to 22.52 m, while reducing the root mean squared error and mean absolute error by 53% and 48%, respectively, and increasing the coefficient of determination (R 2) to 0.98.

Design of computationally efficient density-based clustering algorithms

In the new era of large-scale astronomical surveys, automated methods of analysis and classification of bulk data are a fundamental tool for fast and efficient production of deliverables. This becomes ever more imminent as we enter the Gaia era. We investigate the potential detectability of eclipsing binaries with Gaia using a data set of all Kepler eclipsing binaries sampled with Gaia cadence and folded with the Kepler period. The performance of fitting methods is evaluated with comparison to real Kepler data parameters and a classification scheme is proposed for the potentially detectable sources based on the geometry of the light curve fits. The polynomial chain (polyfit) and two-Gaussian models are used for light curve fitting of the data set. Classification is performed with a combination of the t-SNE (t-distrubuted Stochastic Neighbor Embedding) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithms. We find that approximately 68% of Kepler Eclipsing Binary sources are potentially detectable by Gaia when folded with the Kepler period and propose a classification scheme of the detectable sources based on the morphological type indicative of the light curve, with subclasses that reflect the properties of the fitted model (presence and visibility of eclipses, their width, depth, etc.).