Robust Parameter Optimisation of Noise-Tolerant Clustering for DENCLUE Using Differential Evolution

Spatial clustering analysis is an important spatial data mining technique. It divides objects into clusters according to their similarities in both location and attribute aspects. It plays an essential role in density distribution identification, hot-spot detection, and trend discovery. Spatial clustering algorithms in the Euclidean space are relatively mature, while those in the network space are less well researched. This study aimed to present a well-known clustering algorithm, named density-based spatial clustering of applications with noise (DBSCAN), to network space and proposed a new clustering algorithm named network space DBSCAN (NS-DBSCAN). Basically, the NS-DBSCAN algorithm used a strategy similar to the DBSCAN algorithm. Furthermore, it provided a new technique for visualizing the density distribution and indicating the intrinsic clustering structure. Tested by the points of interest (POI) in Hanyang district, Wuhan, China, the NS-DBSCAN algorithm was able to accurately detect the high-density regions. The NS-DBSCAN algorithm was compared with the classical hierarchical clustering algorithm and the recently proposed density-based clustering algorithm with network-constraint Delaunay triangulation (NC_DT) in terms of their effectiveness. The hierarchical clustering algorithm was effective only when the cluster number was well specified, otherwise it might separate a natural cluster into several parts. The NC_DT method excessively gathered most objects into a huge cluster. Quantitative evaluation using four indicators, including the silhouette, the R-squared index, the Davis–Bouldin index, and the clustering scheme quality index, indicated that the NS-DBSCAN algorithm was superior to the hierarchical clustering and NC_DT algorithms.

Flood is one of the most destructive natural disasters in Malaysia. To understand flood characteristics, we investigate the effectiveness of unsupervised machine learning techniques to identify clusters of flood events based on historical flood data provided by the Department of Irrigation and Drainage Sabah. The dataset used in this study consists of hydrological features such as average recurrence interval, flood duration, flood depth, and affected areas. Due to the nature of raw dataset such as missing data, varying scales, and outliers, we generated six different preprocessed datasets using an extensive data preprocessing which includes handling of missing values, feature normalization, and outlier removal. We then applied five clustering algorithms, which are K-means, K-medoids, hierarchical clustering, density-based spatial clustering of applications with noise (DBSCAN) and its extended version, HDBSCAN, to derive natural clustering in the flood events. The clustering quality was carefully assessed using internal validation metrics such as silhouette score, Davies-Bouldin index, and two-dimensional visualization by principal component analysis. We found that K-means, K-medoids, and hierarchical clustering outperformed DBSCAN and HDBSCAN when a predefined number of clusters was required. The best clustering quality was achieved using datasets with imputation and normalization. Our study provides meaningful insights into the characteristics of flood events.

The purpose of the paper is to extract the region of interest (ROI) from the coarse detected synthetic aperture radar (SAR) images and discriminate if the RO I contains a target or not, so as to eliminate the false alarm, and prepare for the target recognition. Th e automatic target clustering is one of the most difficult tasks in the SAR-image automatic target recognition system. The density-based spatial clustering of applications with noise (DBSCAN) relies on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN was first used in the SAR image processing, which has many excellent features: only two insensitivity parameters (radius of neighborhood and minimum number of points) are needed; clusters of arbitrary shapes which fit in with the coarse detected SAR images can be discovered; and the cal culation time and memory can be reduced. In the multi-feature ROI discrimination scheme, we extract several target features which contain the geometry features such asthe area discriminator and Radon -transform based target profile discriminator, the distribution characteristics such as the EFF discrimina tor, and the EM scattering property such as the PPR discriminator. The synthesized judgment effectively eliminates the false alarms. Keywords: automatic target recognition, synthetic aperture radar images,

<span lang="EN-US">Automatic identification system (AIS) is a vessel radio navigation equipment that has been determined by international maritime organization (IMO). Historical AIS data can be utilized for anomaly detection, trajectory prediction, and vessel trajectory planning. These benefits can be achieved by identifying the vessel's trajectory pattern through trajectory clustering. However, more effort is needed in trajectory clustering using AIS data due to their large volume and the significant number of deficiencies. In addition, trajectory clustering cannot be directly applied to trajectory data, which also applies to vessel trajectory. Therefore, we propose a trajectory clustering framework by combining douglas peucker (DP), longest common subsequence (LCSS), multi-dimensional scaling (MDS), and density-based spatial clustering of applications with noise (DBSCAN). Our experiments, carried out with AIS data for the Lombok Strait, Indonesia, showed that the trajectory compression with DP significantly accelerates the similarity measurement process. Moreover, we found that the LCSS is the optimal algorithm for similarity measurement of vessel trajectories based on AIS data. We also applied the right combination of MDS and DBSCAN in density-based clustering. The proposed framework can distinguish trajectoriess in different directions, identify the noise, and produce good quality clusters in relatively fast total processing time.</span>

Design of computationally efficient density-based clustering algorithms

Image segmentation is the process to divide a digital image into a number of regions for further usage. Color images can be segmented by applying density-based clustering methods, e.g. Density-Based Spatial Clustering of Applications with Noise (DBSCAN), which is used to identify the arbitrary shaped clusters. The drawback of DBSCAN is the high computational complexity whilst the size of image input is normally very large. Self-Organizing Map (SOM) is a dimensionality reduction method which can be applied to reduce the dimensions of image processing tasks. This paper proposes a hybrid method of SOM and DBSCAN (SOM-DBSCAN) for image segmentation. To evaluating the usability of the proposed SOM-DBSCAN method, four images are used to benchmark image segmentation.

Tremendous growth of Location-based technologies resulted in the generation of a huge volume of spatial data, which needs to be analyzed to get potentially important patterns. The spatial patterns extracted can be used to design a better infrastructure ensuring reliable service coverage. Trajectory data is one variant of spatial data that are generated by moving objects travelling across. It is represented as a sequence of spatial coordinates (latitude, longitude) of a location. Trajectory clustering tries to group similar spatial data points to extract the most common movement behaviors. Trajectory data poses major challenges including uncertainty, sampling rate, representation, relationships, spatial autocorrelation, serialization, redundancy, and triviality, which makes it hard to apply traditional clustering algorithms over trajectory data. In this paper, K-Means and DBSCAN (Density-based spatial clustering of applications with noise) clustering algorithms are analyzed using different similarity measures like Euclidean, Hausdorff and Haversine distances with the help of index measures say Adjusted Rand Index (ARI) and Fowlkes-Mallows scores (FMS). Experiment is carried out over two different trajectory datasets and it is proved that usage of Haversine distance for clustering is efficient than Euclidean and Hausdorff distances in terms of spatial trajectory data.

Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a widely used clustering algorithm renowned for its ability to identify clusters of arbitrary shapes and detect noise. However, its reliance on fixed parameters, such as the minimum number of points (MinPts) and the epsilon radius (epsilon), makes it sensitive to variations in sample density. This paper reinterprets DBSCAN as a specific case of kernel density estimation (KDE)-based clustering, where the kernel shape corresponds to a hyper-rectangular pillar or cylindrical kernel, depending on the distance metric. Building on this foundation, we introduce a flexible framework incorporating various kernel functions, including uniform, conical, Epanechnikov, cosine, exponential, and Gaussian kernels, to estimate the density distribution of data points. The threshold values are selected to identify high-density regions by retaining the top 90% of points, while excluding low-density points as noise, thereby enhancing clustering precision. Clusters are adaptively formed by leveraging points within the kernel range, thereby increasing the algorithm's robustness to noise and its adaptability to irregular density patterns. Empirical results demonstrate that the proposed approach outperforms traditional DBSCAN, as evidenced by lower Davies-Bouldin indices and higher silhouette scores. This study highlights the potential of density-driven clustering for practical applications, including social media sentiment analysis, customer segmentation in e-commerce, and medical data analysis, particularly in scenarios involving noise-prone or unevenly distributed datasets.

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

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.

This study investigates the application of maritime trajectory clustering in the Straits of Malacca and Singapore (SOMAS), aiming to identify and categorize key movement patterns in this densely trafficked and complex region. Using Automatic Identification System (AIS) data, the research employs Dynamic Time Warping (DTW) to measure trajectory similarity and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for clustering. Due to the wide variation in trajectory lengths across the region, a statistical grouping technique based on the interquartile range (IQR) is applied to improve clustering performance and manage trajectory diversity. The results demonstrate the successful identification of major traffic routes within the study area, providing valuable insights into dominant vessel movement behaviours and navigation patterns. These findings are essential for supporting maritime traffic analysis, thereby enhancing safe, efficient, and sustainable traffic management in high-density international waterways.

Raman spectroscopy (RS) is a widely used analytical technique based on the detection of molecular vibrations in a defined system, which generates Raman spectra that contain unique and highly resolved fingerprints of the system. However, the low intensity of normal Raman scattering effect greatly hinders its application. Recently, the newly emerged surface enhanced Raman spectroscopy (SERS) technique overcomes the problem by mixing metal nanoparticles such as gold and silver with samples, which greatly enhances signal intensity of Raman effects by orders of magnitudes when compared with regular RS. In clinical and research laboratories, SERS provides a great potential for fast, sensitive, label-free, and non-destructive microbial detection and identification with the assistance of appropriate machine learning (ML) algorithms. However, choosing an appropriate algorithm for a specific group of bacterial species remains challenging, because with the large volumes of data generated during SERS analysis not all algorithms could achieve a relatively high accuracy. In this study, we compared three unsupervised machine learning methods and 10 supervised machine learning methods, respectively, on 2,752 SERS spectra from 117 Staphylococcus strains belonging to nine clinically important Staphylococcus species in order to test the capacity of different machine learning methods for bacterial rapid differentiation and accurate prediction. According to the results, density-based spatial clustering of applications with noise (DBSCAN) showed the best clustering capacity (Rand index 0.9733) while convolutional neural network (CNN) topped all other supervised machine learning methods as the best model for predicting Staphylococcus species via SERS spectra (ACC 98.21%, AUC 99.93%). Taken together, this study shows that machine learning methods are capable of distinguishing closely related Staphylococcus species and therefore have great application potentials for bacterial pathogen diagnosis in clinical settings.

India's agriculture sector holds immense significance in its economy, and this study delves into how different clustering techniques can enhance our comprehension of the interplay between weather patterns and chickpea production in Dharwad district, specifically Density-Based Spatial Clustering of Applications with Noise (DBSCAN), K-Means, and Hierarchical clustering methods to discern underlying patterns within climate variables and chickpea yield data. Leveraging historical weather data spanning an extensive 42-year period and crop yield records were employed to train and validated. The findings illuminate that K-Means clustering consistently outperforms DBSCAN and Hierarchical clustering when evaluated through a variety of validity indices, including the Silhouette Coefficient, Calinski-Harabasz Index, Davies-Bouldin Index, and the determination of the optimal number of clusters. In essence, India's pivotal agricultural sector dynamics and the intricate relationship between climatic factors and chickpea production in Dharwad district are elucidated effectively by the superiority of K-Means clustering in this study.

The standard driving cycles (DCs) used to evaluate spark-ignition engine-based two-wheelers are inadequate for electric two-wheelers (E2Ws). Also, they fail to accurately represent the actual driving circumstances in specific areas, resulting in inaccuracies during the evaluation of performance. The current research is centred towards constructing an electric two-wheeler urban driving cycle (E2WUDC) that considers the driving circumstances of the smart city in India. Further, the denoised speed data is utilized to extract the micro-trips and compute their driving parameters. Furthermore, the dimensions of the data are decreased through the utilization of principal component analysis. Subsequently, the data is classified utilizing various clustering methods including k-means, X-means, hierarchical clustering, and density-based spatial clustering of applications with noise (DBSCAN). Then, the Calinski Harabasz index (CHI), Davies-Bouldin index (DBI), and silhouette score are used to assess the homogeneity and completeness of selected clustering algorithms in the data cluster. Overall, the E2WUDC is developed using X-means which is selected as a suitable clustering algorithm based on the performance indices. Also, the key driving features of E2WUDC such as total time duration and distance are 14.49 km and 1914 seconds with average and maximum driving speeds of 8 and 13.88 m/s respectively. Eventually, it establishes the foundation for assessing the energy economy, driving range and energy demand for the widespread deployment of electric two-wheelers in urban commuting.

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.