GRIDBSCAN: GRId Density-Based Spatial Clustering of Applications with Noise

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

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.

Design of computationally efficient density-based clustering algorithms

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

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.

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.

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

<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>

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.

PurposeRecent investigations failed to reproduce the positive rotor-guided ablation outcomes shown by initial studies for treating persistent atrial fibrillation (persAF). Phase singularity (PS) is an important feature for AF driver detection, but algorithms for automated PS identification differ. We aim to investigate the performance of four different techniques for automated PS detection.Methods2048-channel virtual electrogram (VEGM) and electrocardiogram signals were collected for 30 s from 10 patients undergoing persAF ablation. QRST-subtraction was performed and VEGMs were processed using sinusoidal wavelet reconstruction. The phase was obtained using Hilbert transform. PSs were detected using four algorithms: (1) 2D image processing based and neighbor-indexing algorithm; (2) 3D neighbor-indexing algorithm; (3) 2D kernel convolutional algorithm estimating topological charge; (4) topological charge estimation on 3D mesh. PS annotations were compared using the structural similarity index (SSIM) and Pearson’s correlation coefficient (CORR). Optimized parameters to improve detection accuracy were found for all four algorithms using Fβ score and 10-fold cross-validation compared with manual annotation. Local clustering with density-based spatial clustering of applications with noise (DBSCAN) was proposed to improve algorithms 3 and 4.ResultsThe PS density maps created by each algorithm with default parameters were poorly correlated. Phase gradient threshold and search radius (or kernels) were shown to affect PS detections. The processing times for the algorithms were significantly different (p < 0.0001). The Fβ scores for algorithms 1, 2, 3, 3 + DBSCAN, 4 and 4 + DBSCAN were 0.547, 0.645, 0.742, 0.828, 0.656, and 0.831. Algorithm 4 + DBSCAN achieved the best classification performance with acceptable processing time (2.0 ± 0.3 s).ConclusionAF driver identification is dependent on the PS detection algorithms and their parameters, which could explain some of the inconsistencies in rotor-guided ablation outcomes in different studies. For 3D triangulated meshes, algorithm 4 + DBSCAN with optimal parameters was the best solution for real-time, automated PS detection due to accuracy and speed. Similarly, algorithm 3 + DBSCAN with optimal parameters is preferred for uniform 2D meshes. Such algorithms – and parameters – should be preferred in future clinical studies for identifying AF drivers and minimizing methodological heterogeneities. This would facilitate comparisons in rotor-guided ablation outcomes in future works.

Background: This study focuses on the latest knowledge regarding Micro, Small and Medium Enterprises (MSMEs) as a current central issue. These enterprises have shown their significance in providing employment opportunities and contributing to the country’s economy. However, MSMEs face various challenges that must be addressed to optimize their outcomes. Understanding the characteristics of this group was crucial in formulating effective strategies. Objective: This study proposed to cluster or combine micro, small, and medium enterprises (MSMEs) data in a particular area based on asset value and turnover. As a result, this study aimed to gain insights into the MSME landscape in the area and provided valuable information for decision-makers and stakeholders. Methods: This study utilized two methods, namely the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) method and the K-Means method. These methods were chosen for their distinct capabilities. DBSCAN was selected for its ability to handle noisy data and identify clusters with diverse forms, while K-Means was chosen for its popularity and ability to group data based on proximity. The study used a dataset containing MSME information, including asset values and turnover, collected from various sources. Results: The outcomes encompassed identifying clusters of MSMEs based on their closeness in the feature space within a specific region. Optimizing the clustering outcomes involved modifying algorithm parameters like epsilon and minimum points for DBSCAN and the number of clusters for K-Means. Furthermore, this study attained a deeper understanding of the arrangement and characteristics of MSME clusters in the region through a comparative analysis of the two methodologies. Conclusion: This study offered perspectives on clustering MSMEs based on asset value and turnover in a specific region. Employing DBSCAN and K-Means methodologies allowed researchers to depict the MSME landscape and grasp the business attributes of these enterprises. These results could aid in decision-making and strategic planning concerning the advancement of the MSME sector in the mentioned area. Future study may investigate supplementary factors and variables to deepen comprehension of MSME clusters and promote regional growth and sustainability. Keywords: Asset Value, Clustering, DBSCAN, K-Means, Turnover

Protein sequence data analysis is a fundamental task in bioinformatics, supporting the exploration of biological variations and the identification of functional relationships among proteins. This study presents a performance analysis of four clustering algorithms, which include Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Agglomerative Hierarchical Clustering, and Spectral Clustering, applied to protein sequence datasets. Feature extraction was conducted using the Discere package in Python, generating 27 numerical attributes from protein sequences. The optimal number of clusters for BIRCH, Agglomerative, and Spectral Clustering was determined using the Elbow method, while DBSCAN parameters (MinPts, Eps) were tuned using the sorted k-distance plot. Clustering performance was assessed using the Silhouette Score. Among the algorithms, DBSCAN produced the highest silhouette score of 0.8105, whereas BIRCH achieved a strong balance between clustering quality, with a score of 0.7405, and computational efficiency. Agglomerative clustering provided moderate results with a score of 0.6779, while Spectral clustering yielded the lowest score of 0.6310 but demonstrated flexibility in capturing complex structures. These findings provide a benchmark comparison of clustering methods for protein sequence data, offering practical insights into algorithm selection based on data characteristics and performance trade-offs.

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

This study aims to objectively analyze the feasibility of prospective recipients of the Smart Indonesia Card Scholarship (KIP-K) by integrating the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The research dataset consists of 287 data on prospective scholarship recipients with 11 main attributes that reflect the socio-economic and academic conditions of students. The research process includes data collection, pre-processing, transformation of categorical attributes into numerical values using a linear weighting scheme, cluster analysis using DBSCAN, and candidate ranking using TOPSIS. DBSCAN is used to identify cluster patterns and detect anomalies in the data of potential recipients, while TOPSIS is used to rank candidates based on proximity to the ideal solution. The results of the grouping produced 10 clusters and one noise cluster that showed a variety of socio-economic characteristics of prospective scholarship recipients. The results of the ranking show that some of the candidates with the highest TOPSIS scores come from clusters with higher levels of economic vulnerability. In addition, some of the high-scoring candidates also came from the noise cluster, indicating that even though they did not belong to a particular group, they still met the eligibility criteria based on a multi-criteria evaluation. These findings show that the combination of DBSCAN and TOPSIS has the potential to support the process of analyzing the eligibility of scholarship recipients in a more systematic and data-driven manner.