Journal articles: 'Dbsan'

1

Al-Ameri, Mohammed Abdulbasit Ali, Basim Mahmood, Bünyamin Ciylan, and Alaa Amged. "Unsupervised Forgery Detection of Documents: A Network-Inspired Approach." Electronics 12, no. 7 (April 3, 2023): 1682. http://dx.doi.org/10.3390/electronics12071682.

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The area of forgery detection of documents is considered an active field of research in digital forensics. One of the most common issues that investigators struggle with is circled around the selection of the approach in terms of accuracy, complexity, cost, and ease of use. The literature includes many approaches that are based on either image processing techniques or spectrums analysis. However, most of the available approaches have issues related to complexity and accuracy. This article suggests an unsupervised forgery detection framework that utilizes the correlations among the spectrums of documents’ matters in generating a weighted network for the tested documents. The network, then, is clustered using several unsupervised clustering algorithms. The detection rate is measured according to the number of network clusters. Based on the obtained results, our approach provides high accuracy using the Louvain clustering algorithms, while the use of the updated version of the DBSAN was more successful when testing many documents at the same time. Additionally, the suggested framework is considered simple to implement and does not require professional knowledge to use.

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Rodríguez L., Ingrid, César Honorio J., Julia Ramírez S., Zara León G., and Willman Alarcón G. "Efecto de un anticoccidial natural a base de saponinas de Yucca schidigera y Trigonella foenum-graecum sobre el control de coccidiosis en pollos de carne." Revista de Investigaciones Veterinarias del Perú 30, no. 3 (October 10, 2019): 1196–206. http://dx.doi.org/10.15381/rivep.v30i3.16597.

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El objetivo del estudio fue evaluar el efecto de un anticoccidial natural a base de saponinas procedentes de Yucca schidigera y Trigonella foenum-graecumen en el control de coccidiosis en pollos de carne. Se utilizaron 75 pollos machos de la línea Cobb 500 de un día de edad, distribuidos al azar en tres tratamientos: DBSA (dieta base sin adición de anticoccidial), DBAN (dieta base con adición de anticoccidial natural), DBQI (dieta base con adición de anticoccidial químico - ionóforo) con cinco repeticiones y cinco aves por unidad experimental. Los pollos fueron inoculados en día 14 con 15 veces la dosis recomendada de la vacuna viva (Coccivac-D), que contiene Eimeria acervulina, E. maxima, E. mivati, E. tenella, E. necatrix, E. brunetti, E. hagani y E. praecox para inducir la enfermedad. Se evaluaron las variables recuento de ooquistes por gramo de heces (ROpgh), lesiones intestinales y diversos parámetros productivos. Los valores de ROpgh fueron similares para DBAN y DBQI llegando a su pico a la séptima semana (328.8 y 455.8 Opgh, respectivamente), para luego disminuir. Las lesiones intestinales los días 10, 20 y 28 pos-infección y los parámetros productivos al final del estudio fueron similares para ambos tratamientos, pero significativamente mejores que el control DBSA. Se concluye que las saponinas procedentes de Y. schidigera y T. foenum-graecum pueden remplazar eficientemente a los anticoccidiales químicos en la alimentación de las aves.

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Lv, Yikun, He Jiang, and Pinchen Pan. "NI-DBSCAN: DBSCAN under Non-IID." Journal of Physics: Conference Series 1533 (April 2020): 022110. http://dx.doi.org/10.1088/1742-6596/1533/2/022110.

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Lulli, Alessandro, Matteo Dell'Amico, Pietro Michiardi, and Laura Ricci. "NG-DBSCAN." Proceedings of the VLDB Endowment 10, no. 3 (November 2016): 157–68. http://dx.doi.org/10.14778/3021924.3021932.

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Giri, Kinsuk, Tuhin Kr Biswas, and Pritisha Sarkar. "ECR-DBSCAN: An improved DBSCAN based on computational geometry." Machine Learning with Applications 6 (December 2021): 100148. http://dx.doi.org/10.1016/j.mlwa.2021.100148.

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Feng, Ling, Kejian Liu, Fuxi Tang, and Qingrui Meng. "GO-DBSCAN: Improvements of DBSCAN Algorithm Based on Grid." International Journal of Computer Theory and Engineering 9, no. 3 (2017): 151–55. http://dx.doi.org/10.7763/ijcte.2017.v9.1129.

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Schubert, Erich, Jörg Sander, Martin Ester, Hans Peter Kriegel, and Xiaowei Xu. "DBSCAN Revisited, Revisited." ACM Transactions on Database Systems 42, no. 3 (August 24, 2017): 1–21. http://dx.doi.org/10.1145/3068335.

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Chen, Guangsheng, Yiqun Cheng, and Weipeng Jing. "DBSCAN-PSM: an improvement method of DBSCAN algorithm on Spark." International Journal of High Performance Computing and Networking 13, no. 4 (2019): 417. http://dx.doi.org/10.1504/ijhpcn.2019.099265.

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Jing, Weipeng, Guangsheng Chen, and Yiqun Cheng. "DBSCAN-PSM: an improvement method of DBSCAN algorithm on Spark." International Journal of High Performance Computing and Networking 13, no. 4 (2019): 417. http://dx.doi.org/10.1504/ijhpcn.2019.10020624.

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Cheng, Dongdong, Cheng Zhang, Ya Li, Shuyin Xia, Guoyin Wang, Jinlong Huang, Sulan Zhang, and Jiang Xie. "GB-DBSCAN: A fast granular-ball based DBSCAN clustering algorithm." Information Sciences 674 (July 2024): 120731. http://dx.doi.org/10.1016/j.ins.2024.120731.

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11

Eisazadeh, Hossein, and Hamid Reza Khorshidi. "Preparation and characterization of polyaniline-DBSNa/Fe2O3and polyaniline-DBSNa/CoO nanocomposites using surfactive dopant sodium dodecylbenzenesulfonate (DBSNa)." Journal of Vinyl and Additive Technology 16, no. 1 (March 2010): 105–10. http://dx.doi.org/10.1002/vnl.20214.

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12

Pratama, Alfan Rizaldy, Bima Sena Bayu Dewantara, Dewi Mutiara Sari, and Dadet Pramadihanto. "Improvement of DBSCAN Algorithm Involving Automatic Parameters Estimation and Curvature Analysis in 3D Point Cloud of Piled Pipe." Journal of Image and Graphics 12, no. 2 (2024): 175–85. http://dx.doi.org/10.18178/joig.12.2.175-185.

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Bin-picking in the industrial area is a challenging task since the object is piled in a box. The rapid development of 3D point cloud data in the bin-picking task has not fully addressed the robustness issue of handling objects in every circumstance of piled objects. Density-Based Spatial Clustering of Application with Noise (DBSCAN) as the algorithm that attempts to solve by its density still has a disadvantage like parameter-tuning and ignoring the unique shape of an object. This paper proposes a solution by providing curvature analysis in each point data to represent the shape of an object therefore called Curvature-Density-Based Spatial Clustering of Application with Noise (CVR-DBSCAN). Our improvement uses curvature to analyze object shapes in different placements and automatically estimates parameters like Eps and MinPts. Divided by three algorithms, we call it Auto-DBSCAN, CVR-DBSCAN-Avg, and CVR-DBSCAN-Disc. By using real-scanned Time-of-Flight camera datasets separated by three piled conditions that are well separated, well piled, and arbitrary piled to analyze all possibilities in placing objects. As a result, in well separated, Auto-DBSCAN leads by the stability and accuracy in 99.67% which draws as the DBSCAN using specified parameters. For well piled, CVR-DBSCAN-Avg gives the highest stability although the accuracy can be met with DBSCAN on specified parameters in 98.83%. Last, in arbitrary piled though CVR-DBSCAN-Avg in accuracy lower than DBSCAN which is 73.17% compared to 80.43% the stability is slightly higher with less outlier value. Deal with computational time higher than novel DBSCAN, our improvement made the simplicity and deep analysis in scene understanding.

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Dillon, Pitisit, Pakinee Aimmanee, Akihiko Wakai, Go Sato, Hoang Viet Hung, and Jessada Karnjana. "A Novel Recursive Non-Parametric DBSCAN Algorithm for 3D Data Analysis with an Application in Rockfall Detection." Journal of Disaster Research 16, no. 4 (June 1, 2021): 579–87. http://dx.doi.org/10.20965/jdr.2021.p0579.

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The density-based spatial clustering of applications with noise (DBSCAN) algorithm is a well-known algorithm for spatial-clustering data point clouds. It can be applied to many applications, such as crack detection, rockfall detection, and glacier movement detection. Traditional DBSCAN requires two predefined parameters. Suitable values of these parameters depend upon the distribution of the input point cloud. Therefore, estimating these parameters is challenging. This paper proposed a new version of DBSCAN that can automatically customize the parameters. The proposed method consists of two processes: initial parameter estimation based on grid analysis and DBSCAN based on the divide-and-conquer (DC-DBSCAN) approach, which repeatedly performs DBSCAN on each cluster separately and recursively. To verify the proposed method, we applied it to a 3D point cloud dataset that was used to analyze rockfall events at the Puiggcercos cliff, Spain. The total number of data points used in this study was 15,567. The experimental results show that the proposed method is better than the traditional DBSCAN in terms of purity and NMI scores. The purity scores of the proposed method and the traditional DBSCAN method were 96.22% and 91.09%, respectively. The NMI scores of the proposed method and the traditional DBSCAN method are 0.78 and 0.49, respectively. Also, it can detect events that traditional DBSCAN cannot detect.

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Li, Yujun, Zhi Yang, Shangbin Jiao, and Yuxing Li. "Partition KMNN-DBSCAN Algorithm and Its Application in Extraction of Rail Damage Data." Mathematical Problems in Engineering 2022 (July 13, 2022): 1–10. http://dx.doi.org/10.1155/2022/4699573.

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In order to realize intelligent identification of rail damage, this paper studies the extraction method of complete damage ultrasonic B-scan data based on the density-based spatial clustering of applications with noise algorithm (DBSCAN). Aiming at the problem that the traditional DBSCAN algorithm needs to manually set the Eps and Minpts parameters, a KMNN-DBSCAN (K-median nearest neighbor DBSCAN) algorithm is proposed. The algorithm first uses the dataset’s own distribution characteristics to generate a list of Eps and Minpts parameters and then determines the optimal Eps and Minpts through an optimization strategy to achieve complete self-adaptation of the two parameters of Eps and Minpts. In order to further improve the clustering performance of the algorithm, the partition idea is introduced, and the partition KMNN-DBSCAN algorithm is proposed to solve the problem that the clustering results of the DBSCAN algorithm are inconsistent with the actual categories on datasets with uneven density. The experimental results show that the KMNN-DBSCAN algorithm has higher clustering accuracy and silhouette coefficient (SC) for the D037 dataset ultrasound information group (UIG) division; compared with the KMNN-DBSCAN algorithm, the proposed partition KMNN-DBSCAN algorithm has higher clustering accuracy, F-Measure, and SC values. The partition KMNN-DBSCAN algorithm achieves accurate division of all damage UIG on the damaged B-scan data with large density differences, and completes the effective extraction of complete damage data.

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Amiruzzaman, Md, Rashik Rahman, Md Rajibul Islam, and Rizal Mohd Nor. "Logical analysis of built-in DBSCAN Functions in Popular Data Science Programming Languages." MIST INTERNATIONAL JOURNAL OF SCIENCE AND TECHNOLOGY 10 (June 26, 2022): 25–32. http://dx.doi.org/10.47981/j.mijst.10(01)2022.349(25-32).

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DBSCAN algorithm is a location-based clustering approach; it is used to find relationships and patterns in geographical data. Because of its widespread application, several data science-based programming languages include the DBSCAN method as a built-in function. Researchers and data scientists have been clustering and analyzing their study data using the built-in DBSCAN functions. All implementations of the DBSCAN functions require user input for radius distance (i.e., eps) and a minimum number of samples for a cluster (i.e., min_sample). As a result, the result of all built-in DBSCAN functions is believed to be the same. However, the DBSCAN Python built-in function yields different results than the other programming languages those are analyzed in this study. We propose a scientific way to assess the results of DBSCAN built-in function, as well as output inconsistencies. This study reveals various differences and advises caution when working with built-in functionality.

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WANG, Gui-zhi, and Guang-liang WANG. "Improved fast DBSCAN algorithm." Journal of Computer Applications 29, no. 9 (November 3, 2009): 2505–8. http://dx.doi.org/10.3724/sp.j.1087.2009.02505.

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17

He, Yaobin, Haoyu Tan, Wuman Luo, Shengzhong Feng, and Jianping Fan. "MR-DBSCAN: a scalable MapReduce-based DBSCAN algorithm for heavily skewed data." Frontiers of Computer Science 8, no. 1 (December 19, 2013): 83–99. http://dx.doi.org/10.1007/s11704-013-3158-3.

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18

Kim, Jeong-Hun, Jong-Hyeok Choi, Kwan-Hee Yoo, and Aziz Nasridinov. "AA-DBSCAN: an approximate adaptive DBSCAN for finding clusters with varying densities." Journal of Supercomputing 75, no. 1 (May 8, 2018): 142–69. http://dx.doi.org/10.1007/s11227-018-2380-z.

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Jungan, Chen, Chen Jinyin, Yang Dongyong, and Li Jun. "A k-Deviation Density Based Clustering Algorithm." Mathematical Problems in Engineering 2018 (2018): 1–16. http://dx.doi.org/10.1155/2018/3742048.

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Due to the adoption of global parameters, DBSCAN fails to identify clusters with different and varied densities. To solve the problem, this paper extends DBSCAN by exploiting a new density definition and proposes a novel algorithm called k-deviation density based DBSCAN (kDDBSCAN). Various datasets containing clusters with arbitrary shapes and different or varied densities are used to demonstrate the performance and investigate the feasibility and practicality of kDDBSCAN. The results show that kDDBSCAN performs better than DBSCAN.

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Yu, Zhenhao, Fang Liu, Yinquan Yuan, Sihan Li, and Zhengying Li. "Signal Processing for Time Domain Wavelengths of Ultra-Weak FBGs Array in Perimeter Security Monitoring Based on Spark Streaming." Sensors 18, no. 9 (September 4, 2018): 2937. http://dx.doi.org/10.3390/s18092937.

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To detect perimeter intrusion accurately and quickly, a stream computing technology was used to improve real-time data processing in perimeter intrusion detection systems. Based on the traditional density-based spatial clustering of applications with noise (T-DBSCAN) algorithm, which depends on manual adjustments of neighborhood parameters, an adaptive parameters DBSCAN (AP-DBSCAN) method that can achieve unsupervised calculations was proposed. The proposed AP-DBSCAN method was implemented on a Spark Streaming platform to deal with the problems of data stream collection and real-time analysis, as well as judging and identifying the different types of intrusion. A number of sensing and processing experiments were finished and the experimental data indicated that the proposed AP-DBSCAN method on the Spark Streaming platform exhibited a fine calibration capacity for the adaptive parameters and the same accuracy as the T-DBSCAN method without the artificial setting of neighborhood parameters, in addition to achieving good performances in the perimeter intrusion detection systems.

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Hasana, Silviya, and Devi Fitrianah. "A Study on Enhanced Spatial Clustering Using Ensemble DBscan and UMAP to Map Fire Zone in Greater Jakarta, Indonesia." Jurnal Riset Informatika 5, no. 3 (June 10, 2023): 409–18. http://dx.doi.org/10.34288/jri.v5i3.557.

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This research investigated ensemble clustering algorithms and dimensionality reduction for fire zone mapping, specifically DBSCAN + UMAP. We evaluated six clustering methods: DBSCAN, ensemble DBSCAN, DBSCAN + UMAP, ensemble DBSCAN + UMAP, HDBSCAN and Gaussian Mixture Model (GMM). We evaluated our results based on the Silhouette Score and the Davies-Bouldin (DB) index, emphasizing handling irregular cluster shapes, smaller clusters and resolving incompact clusters. Our findings suggested that ensemble DBSCAN + UMAP outperformed five other methods with zero noise clusters indicating clustering results are resistant to outliers, leading to a clearer identification of fire-prone areas, a high Silhouette Score of 0.971, indicating accurate cluster separation of distinct areas of potential fire hazards and an exceptionally low DB Index of 0.05 that indicates compact clusters to identify well-defined and geographically concentrated areas prone to fire hazards. Our findings contribute to the advanced techniques for minimizing the impacts of fires and improving fire hazard assessments in Indonesia.

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Hasana, Silviya, and Devi Fitrianah. "A Study on Enhanced Spatial Clustering Using Ensemble Dbscan and Umap to Map Fire Zone in Greater Jakarta, Indonesia." Jurnal Riset Informatika 5, no. 3 (June 23, 2023): 409–18. http://dx.doi.org/10.34288/jri.v5i3.234.

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This research investigated ensemble clustering algorithms and dimensionality reduction for fire zone mapping, specifically DBSCAN + UMAP. We evaluated six clustering methods: DBSCAN, ensemble DBSCAN, DBSCAN + UMAP, ensemble DBSCAN + UMAP, HDBSCAN and Gaussian Mixture Model (GMM). We evaluated our results based on the Silhouette Score and the Davies-Bouldin (DB) index, emphasizing handling irregular cluster shapes, smaller clusters and resolving incompact clusters. Our findings suggested that ensemble DBSCAN + UMAP outperformed five other methods with zero noise clusters indicating clustering results are resistant to outliers, leading to a clearer identification of fire-prone areas, a high Silhouette Score of 0.971, indicating accurate cluster separation of distinct areas of potential fire hazards and an exceptionally low DB Index of 0.05 that indicates compact clusters to identify well-defined and geographically concentrated areas prone to fire hazards. Our findings contribute to the advanced techniques for minimizing the impacts of fires and improving fire hazard assessments in Indonesia.

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Fahira, Aisyah Nur, and Rani Nooraeni. "https://jurnal.fmipa.unila.ac.id/komputasi/issue/view/146/showToc." Jurnal Komputasi 11, no. 1 (April 30, 2023): 24–33. http://dx.doi.org/10.23960/komputasi.v11i1.3175.

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Spatio Temporal DBSCAN (ST-DBSCAN) adalah metode yang dapat diterapkan pada data spasial yang diikuti dengan atribut temporal. Hasil dari ST-DBSCAN tergantung pada penentuan awal tiga parameter. Inisial parameter yang tidak optimal menyebabkan hasil pengelompokan dengan ST-DBSCAN tidak mencapai solusi yang global optimum. Penelitian ini bertujuan untuk mengoptimalkan penentuan parameter awal pada ST-DBSCAN menggunakan metode k Nearest neighborhood dan Algoritma Genetika yang diuji menggunakan data simulasi kemudian diterapkan dalam pengelompokan wilayah bencana alam. Hasil yang didapatkan adalah pemilihan parameter yang dioptimasi menggunakan algoritma genetika menghasilkan cluster dengan koefisien CDBw terbesar pada perbandingan evaluasi, akan tetapi perlu waktu yang lama untuk merunning sehingga metode tersebut diuji coba dengan data dengan jumlah observasi sedikit. Hasil dari implementasi metode terhadap data bencana alam menunjukkan terdapat 22 cluster

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Zhou, Wei, Limin Wang, Xuming Han, Yizhang Wang, Yufei Zhang, and Zhiyao Jia. "Adaptive Density Spatial Clustering Method Fusing Chameleon Swarm Algorithm." Entropy 25, no. 5 (May 11, 2023): 782. http://dx.doi.org/10.3390/e25050782.

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The density-based spatial clustering of application with noise (DBSCAN) algorithm is able to cluster arbitrarily structured datasets. However, the clustering result of this algorithm is exceptionally sensitive to the neighborhood radius (Eps) and noise points, and it is hard to obtain the best result quickly and accurately with it. To solve the above problems, we propose an adaptive DBSCAN method based on the chameleon swarm algorithm (CSA-DBSCAN). First, we take the clustering evaluation index of the DBSCNA algorithm as the objective function and use the chameleon swarm algorithm (CSA) to iteratively optimize the evaluation index value of the DBSCAN algorithm to obtain the best Eps value and clustering result. Then, we introduce the theory of deviation in the data point spatial distance of the nearest neighbor search mechanism to assign the identified noise points, which solves the problem of over-identification of the algorithm noise points. Finally, we construct color image superpixel information to improve the CSA-DBSCAN algorithm’s performance regarding image segmentation. The simulation results of synthetic datasets, real-world datasets, and color images show that the CSA-DBSCAN algorithm can quickly find accurate clustering results and segment color images effectively. The CSA-DBSCAN algorithm has certain clustering effectiveness and practicality.

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Mo, Guanlin, Shihong Song, and Hu Ding. "Towards Metric DBSCAN: Exact, Approximate, and Streaming Algorithms." Proceedings of the ACM on Management of Data 2, no. 3 (May 29, 2024): 1–25. http://dx.doi.org/10.1145/3654981.

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DBSCAN is a popular density-based clustering algorithm that has many different applications in practice. However, the running time of DBSCAN in high-dimensional space or general metric space (\em e.g., clustering a set of texts by using edit distance) can be as large as quadratic in the input size. Moreover, most of existing accelerating techniques for DBSCAN are only available for low-dimensional Euclidean space. In this paper, we study the DBSCAN problem under the assumption that the inliers (the core points and border points) have a low intrinsic dimension (which is a realistic assumption for many high-dimensional applications), where the outliers can locate anywhere in the space without any assumption. First, we propose a k-center clustering based algorithm that can reduce the time-consuming labeling and merging tasks of DBSCAN to be linear. Further, we propose a linear time approximate DBSCAN algorithm, where the key idea is building a novel small-size summary for the core points. Also, our algorithm can be efficiently implemented for streaming data and the required memory is independent of the input size. Finally, we conduct our experiments and compare our algorithms with several popular DBSCAN algorithms. The experimental results suggest that our proposed approach can significantly reduce the computational complexity in practice.

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An, Xiaoya, Ziming Wang, Ding Wang, Song Liu, Cheng Jin, Xinpeng Xu, and Jianjun Cao. "STRP-DBSCAN: A Parallel DBSCAN Algorithm Based on Spatial-Temporal Random Partitioning for Clustering Trajectory Data." Applied Sciences 13, no. 20 (October 10, 2023): 11122. http://dx.doi.org/10.3390/app132011122.

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Trajectory clustering algorithms analyze the movement trajectory of the target objects to mine the potential movement trend, regularity, and behavioral patterns of the object. Therefore, the trajectory clustering algorithm has a wide range of applications in the fields of traffic flow analysis, logistics and transportation management, and crime analysis. Existing algorithms do not make good use of the temporal attributes of trajectory data, resulting in a long clustering time and low clustering accuracy of spatial-temporal trajectory data. Meanwhile, the density-based clustering algorithms represented by DBSCAN are very sensitive to the clustering parameters. The radius value Eps and the minimal points number MinPts within Eps radius, defined by the user, have a significant impact on the clustering results, and tuning these parameters is difficult. In this paper, we present STRP-DBSCAN, a parallel DBSCAN algorithm based on spatial-temporal random partitioning for clustering trajectory data. It adopts spatial-temporal random partitioning to distribute balanced computation among different computing nodes and reduce the communication overhead of the parallel clustering algorithm, thus improving the execution efficiency of the DBSCAN algorithm. We also present the PER-SAC algorithm, which uses deep reinforcement learning to combine the prioritized experience replay (PER) and the soft actor-critic (SAC) algorithm for autotuning the optimal parameters of DBSCAN. The experimental results show that STRP-DBSCAN effectively reduces the clustering time of spatial-temporal trajectory data by up to 96.2% and 31.2% compared to parallel DBSCAN and the state-of-the-art RP-DBSCAN. The PER-SAC algorithm also outperforms the state-of-the-art DBSCAN parameter tuning algorithms and improves the clustering accuracy by up to 8.8%. At the same time, the proposed algorithm obtains a higher stability of clustering accuracy.

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Wang, Tianfu, Chang Ren, Yun Luo, and Jing Tian. "NS-DBSCAN: A Density-Based Clustering Algorithm in Network Space." ISPRS International Journal of Geo-Information 8, no. 5 (May 8, 2019): 218. http://dx.doi.org/10.3390/ijgi8050218.

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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.

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Ros, Frédéric, Serge Guillaume, Rabia Riad, and Mohamed El Hajji. "Detection of natural clusters via S-DBSCAN a Self-tuning version of DBSCAN." Knowledge-Based Systems 241 (April 2022): 108288. http://dx.doi.org/10.1016/j.knosys.2022.108288.

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Ma, Li, Lei Gu, Bo Li, Shouyi Qiao, and Jin Wang. "MRG-DBSCAN: An Improved DBSCAN Clustering Method Based on Map Reduce and Grid." International Journal of Database Theory and Application 8, no. 2 (April 30, 2015): 119–28. http://dx.doi.org/10.14257/ijdta.2015.8.2.12.

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Liu, Ying, Qi Wen, Jia Li Guan, Shi Jie Zhao, Qi Xing Hu, Zhi Feng Hou, and Qiao Zhen Yu. "Structure-Property Relationship of Dodecylbenzenesulfonic Acid Doped Polyaniline." Advanced Materials Research 721 (July 2013): 199–205. http://dx.doi.org/10.4028/www.scientific.net/amr.721.199.

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Dodecylbenzenesulfonic acid (DBSA) doped polypanilines (PANIs) were chemically synthesized in different molar ratios of aniline (An) to ammonium persulfate (APS) and An to DBSA. The microstructures of these PANIs were investigated by means of scanning electron microscope (SEM), X-ray diffraction (XRD), and Fourier Transform Infrared (FTIR). UV-Vis spectrometer, semiconductor parameter analyzer, ubbelohde viscometer and electrospinning technique were used to characterize the optical, electrical properties, viscosity and solubility of these PANIs. The results show that the molar rations of An to APS and An to DBSA had strong effect on the microstructure, molecular weight, degree of crystallinity, optical property, solubility and conductivity of obtained DBSA doped PANI. With the increase of the molar ratios of An to APS and An to DBSA, the conductivities and molecular weight of DBSA doped PANIs decreased, while the degree of crystallinity and solubility of DBSA doped PANIs increased. The DBSA doped PANI could dissolve in dichloromethane or HFIP and could be fabricated short fibers by electrospinning. Moreover, the solution of DBSA doped PANIs in concentrated sulphuric acid showed liquid crystal property.

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Esfandiari, Hossein, Vahab Mirrokni, and Peilin Zhong. "Almost Linear Time Density Level Set Estimation via DBSCAN." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (May 18, 2021): 7349–57. http://dx.doi.org/10.1609/aaai.v35i8.16902.

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In this work we focus on designing a fast algorithm for lambda-density level set estimation via DBSCAN clustering. Previous work (Jiang ICML’17, and Jang and Jiang ICML’19) shows that under some natural assumptions DBSCAN and its variant DBSCAN++ can be used to estimate the lambda-density level set with near-optimal Hausdorff distance, i.e., with rate O~(n^{-1/(2 * beta+D)}). However, to achieve this near-optimal rate, the current fastest DBSCAN algorithm needs near quadratic running time. This running time is not very practical for giant datasets. Usually when we are working with very large datasets we desire linear or almost linear time algorithms. With this motivation, in this work, we present a modified DBSCAN algorithm with near optimal Hausdorff distance for density level set estimation with O~(n) running time. In our empirical study, we show that our algorithm provides significant speedup over the previous algorithms, while achieving comparable solution quality.

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Basavaiah, K., K. Tirumala Rao, and A. V. Prasada Rao. "Synthesis and Characterization of Dodecylbenzene Sulfonic Acid doped Tetraaniline via Emulsion Polymerization." E-Journal of Chemistry 9, no. 3 (2012): 1342–46. http://dx.doi.org/10.1155/2012/692853.

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In this work, we report preparation and characterization of dodecylbenzene sulfonic acid (DBSA) doped tetraaniline via micelles assisted method using ammonium per sulphate (APS) as an oxidant. Here, DBSA act as dopant as well as template for tetraaniline nanostructures. The synthesized DBSA doped tetraaniline have been well characterized by X-ray diffraction patterns, Fourier transform infrared spectroscopy, UV-Visible spectroscopy, Scanning electron microscopy and thermogravimetry. The morphologies of tetraaniline were found to be dependent on molar ratios of N-phenyl-1, 4-phenylenediamine to DBSA. The spectroscopic data indicated that DBSA doped tetraaniline. Thermogravimetry studies revealed that the DBSA doping improved the thermal stability of tetraaniline.

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Creţulescu, Radu G., Daniel I. Morariu, Macarie Breazu, and Daniel Volovici. "DBSCAN Algorithm for Document Clustering." International Journal of Advanced Statistics and IT&C for Economics and Life Sciences 9, no. 1 (June 1, 2019): 58–66. http://dx.doi.org/10.2478/ijasitels-2019-0007.

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AbstractDocument clustering is a problem of automatically grouping similar document into categories based on some similarity metrics. Almost all available data, usually on the web, are unclassified so we need powerful clustering algorithms that work with these types of data. All common search engines return a list of pages relevant to the user query. This list needs to be generated fast and as correct as possible. For this type of problems, because the web pages are unclassified, we need powerful clustering algorithms. In this paper we present a clustering algorithm called DBSCAN – Density-Based Spatial Clustering of Applications with Noise – and its limitations on documents (or web pages) clustering. Documents are represented using the “bag-of-words” representation (word occurrence frequency). For this type o representation usually a lot of algorithms fail. In this paper we use Information Gain as feature selection method and evaluate the DBSCAN algorithm by its capacity to integrate in the clusters all the samples from the dataset.

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Zhang, Runfa, Jianlong Qiu, Ming Guo, Huixia Cui, and Xiangyong Chen. "An Adjusting Strategy after DBSCAN." IFAC-PapersOnLine 55, no. 3 (2022): 219–22. http://dx.doi.org/10.1016/j.ifacol.2022.05.038.

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TAN, Ying. "Adapted DBSCAN with multi-threshold." Journal of Computer Applications 28, no. 3 (July 10, 2008): 745–48. http://dx.doi.org/10.3724/sp.j.1087.2008.00745.

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Sadhukhan, Payel, Labani Halder, and Sarbani Palit. "Approximate DBSCAN on obfuscated data." Journal of Information Security and Applications 80 (February 2024): 103664. http://dx.doi.org/10.1016/j.jisa.2023.103664.

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Kuo, Fei-Ying, Tzai-Hung Wen, and Clive E. Sabel. "Characterizing Diffusion Dynamics of Disease Clustering: A Modified Space–Time DBSCAN (MST-DBSCAN) Algorithm." Annals of the American Association of Geographers 108, no. 4 (January 26, 2018): 1168–86. http://dx.doi.org/10.1080/24694452.2017.1407630.

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Wei, Pan, Yang Shenglin, Li Guang, and Jiang Jianming. "Hydrogen Bonding Effects on the Electrical Properties and Phase Morphology of Polyaniline Blends." Polymers and Polymer Composites 13, no. 4 (May 2005): 415–23. http://dx.doi.org/10.1177/096739110501300408.

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Blends of dodecylbenzene sulfonic acid-doped polyaniline (PANI-DBSA) with either polyacrylonitrile copolymer (PAN) or polystyrene (PS) were solution cast. The investigation focused on the interaction between the components, the morphology and the resulting electrical conductivity of blends. The results showed that with the same PANI-DBSA content the conductivity of PANI-DBSA/PAN was higher than that of PANI-DBSA/PS. PANI-DBSA was dispersed uniformly in the PAN matrix and its cluster size was rather smaller than in the PS matrix. This is attributed to hydrogen bonding between the carbonyl groups in PAN and the imine groups in PANI, which should lead to better compatibility between PANI-DBSA and PAN.

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Ahmed, K. Nafees, and T. Abdul Razak. "Density Based Clustering with Integrated One-Class SVM for Noise Reduction." International Journal of Informatics and Communication Technology (IJ-ICT) 6, no. 3 (December 1, 2017): 199. http://dx.doi.org/10.11591/ijict.v6i3.pp199-208.

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Information extraction from data is one of the key necessities for data analysis. Unsupervised nature of data leads to complex computational methods for analysis. This paper presents a density based spatial clustering technique integrated with one-class Support Vector Machine (SVM), a machine learning technique for noise reduction, a modified variant of DBSCAN called Noise Reduced DBSCAN (NRDBSCAN). Analysis of DBSCAN exhibits its major requirement of accurate thresholds, absence of which yields suboptimal results. However, identifying accurate threshold settings is unattainable. Noise is one of the major side-effects of the threshold gap. The proposed work reduces noise by integrating a machine learning classifier into the operation structure of DBSCAN. The Experimental results indicate high homogeneity levels in the clustering process.

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Wang, Wen Qin, Rui Feng Zhang, and Lin Chao Lu. "The Electrochemical Polymerization of Polypyrrole Microstructures in an Aqueous Solution of Dodecylbenzenesulfonic Acid." Advanced Materials Research 306-307 (August 2011): 696–700. http://dx.doi.org/10.4028/www.scientific.net/amr.306-307.696.

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Conducting polymer – polypyrrole (PPy) microstructures were fabricated in an aqueous solution of dodecylbenzenesulfonic acid (DBSA) by electrochemical polymerization. The study implied the concentration of DBSA had great effect on the morphologies and conductivities of PPy microstructures. At lower DBSA concentration (0.2M), no obvious microstructures were generated. Increasing DBSA concentration, some novel “chayote-like”, “flower-like” microstructures were obtained by modulating electrochemical conditions. The growth process of microstructures was studied by scanning electron microscopy and relevant mechanism was discussed. Raman characterizations indicated that the microstructures were made of conductive PPy doped by DBSA.

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Yin, Lifeng, Hongtao Hu, Kunpeng Li, Guanghai Zheng, Yingwei Qu, and Huayue Chen. "Improvement of DBSCAN Algorithm Based on K-Dist Graph for Adaptive Determining Parameters." Electronics 12, no. 15 (July 25, 2023): 3213. http://dx.doi.org/10.3390/electronics12153213.

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For the shortcomings of an unstable clustering effect and low accuracy caused by the manual setting of the two parameters Eps and MinPts of the DBSCAN (density-based spatial clustering of applications with noise) algorithm, this paper proposes an adaptive determination method for DBSCAN algorithm parameters based on the K-dist graph, noted as X-DBSCAN. The algorithm uses the least squares polynomial curve fitting method to fit the curve in the K-dist graph to generate a list of candidate Eps parameters and uses the mathematical expectation method and noise reduction threshold to generate the corresponding MinPts parameter list. According to the clustering results of each group of parameters in the Eps and MinPts parameter lists, a stable range of cluster number changes is found, and the MinPts and Eps corresponding to the maximum K value in the stable range are selected as the optimal algorithm parameters. The optimality of this parameter was verified using silhouette coefficients. A variety of experiments were designed from multiple angles on the artificial dataset and the UCI real dataset. The experimental results show that the clustering accuracy of X-DBSCAN was 21.83% and 15.52% higher than that of DBSCAN on the artificial and real datasets, respectively. The X-DBSCAN algorithm was also superior to other algorithms through comprehensive evaluation and analysis of various clustering indicators. In addition, experiments on four synthetic Gaussian datasets of different dimensions showed that the average clustering indices of the proposed algorithm were above 0.999. The X-DBSCAN algorithm can select parameters adaptively in combination with the characteristics of the dataset; the clustering effect is better, and clustering process automation is realized.

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Gunawan, Wawan. "Implementasi Algoritma DBScan dalam Pemngambilan Data Menggunakan Scatterplot." Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi 6, no. 2 (October 19, 2021): 91–98. http://dx.doi.org/10.36805/technoxplore.v6i2.1179.

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Seiring dengan perkembangan teknologi informasi dan komunikasi, semakin banyak data yang digunakan dalam suatu pemecahan masalah. Tetapi, dengan banyaknya data yang ada sangat sulit mencari informasi yang diinginkan. Oleh karena itu, dilakukan data mining untuk mengekstraksi pengetahuan secara otomatis dari data berukuran besar dengan cara mencari pola-pola menarik yang terkandung di dalam data tersebut. Dalam penelitian ini, peneliti menggunakan algoritma DBSCAN dalam penelitiannya. Data yang digunakan adalah data spasial mahasiswa Universitas Mercu Buana. Dari data ini, peneliti mengambil informasi scatterplot yang terbentuk, lalu dengan algoritma DBSCAN untuk melihat cluster yang terbentuk, dan melakukan validasi dengan Silhouette Index. Dari penelitian ini dapat disimpulkan bahwa algoritma DBSCAN berhasil diimplementasikan pada data mahasiswa Universitas Mercu Buana. Dan hasil pengujian dari implementasi algoritma DBSCAN dipengaruhi oleh dua nilai parameter yaitu Minimum Points, dan Epsilon.

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Zhang, Xinhui, Xun Shen, and Tinghui Ouyang. "Extension of DBSCAN in Online Clustering: An Approach Based on Three-Layer Granular Models." Applied Sciences 12, no. 19 (September 20, 2022): 9402. http://dx.doi.org/10.3390/app12199402.

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In big data analysis, conventional clustering algorithms have limitations to deal with nonlinear spatial datasets, e.g., low accuracy and high computation cost. Aiming at these problems, this paper proposed a new DBSCAN extension algorithm for online clustering, which consists of three layers, considering DBSCAN, granular computing (GrC), and fuzzy rule-based modeling. Firstly, making use of DBSCAN algorithms’ advantages at extracting structural information, spatial data are clustered via DBSCAN into structural clusters, which are subsequently described by structural information granules (IG) via GrC. Secondly, based on the structural IGs, a series of granular models are constructed in the medium space, and utilized to form fuzzy rules to guide clustering on spatial data. Finally, with the help of structural IGs and granular rules, a rule-based modeling method is constructed in the output space for online clustering. Experiments on a synthetic toy dataset and a typical spatial dataset are implemented in this paper. Numerical results validate the feasibility to the proposed method in online spatial data clustering. Moreover, comparative studies with conventional methods and existing DBSCAN variants demonstrate the superiorities of the proposed method, as well as accuracy improvement and computation overhead reduction.

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Kaulage Anant Nagesh,, Et al. "Spatial Data Analysis Utilizing Grid Dbscan Algorithm in Clustering Techniques for Partial Object Classification Issues." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9s (August 31, 2023): 884–88. http://dx.doi.org/10.17762/ijritcc.v11i9s.9711.

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Clustering algorithms to solve problems with partial object categorization in spatial data analysis is the topic of this research, which explores the usefulness of these techniques. In order to do this, the Grid-DBSCAN method is offered as an effective clustering tool for the purpose of resolving issues involving partial object categorization. A grid-based technique is included into the Grid-DBSCAN algorithm, which is derived from the DBSCAN algorithm and is designed to increase its overall performance. A number of datasets taken from the real world are used to evaluate the method, and it is then compared to existing clustering techniques. The findings of the experiments indicate that the Grid-DBSCAN method is superior to the other clustering algorithms in terms of accuracy and resilience, and that it is able to locate the most effective solution for jobs involving partial object categorization. It is also possible to enhance the Grid-DBSCAN technique so that it can handle different kinds of complicated datasets. The purpose of this study is to offer an understanding of the efficiency of the suggested method and its potential to perform partial object categorization problems in spatial data analysis.

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Sakkari, Mohamed, Abeer D. Algarni, and Mourad Zaied. "Urban Crowd Detection Using SOM, DBSCAN and LBSN Data Entropy: A Twitter Experiment in New York and Madrid." Electronics 8, no. 6 (June 20, 2019): 692. http://dx.doi.org/10.3390/electronics8060692.

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The surfer and the physical location are two important concepts associated with each other in the social network-based localization service. This work consists of studying urban behavior based on location-based social networks (LBSN) data; we focus especially on the detection of abnormal events. The proposed crowd detection system uses the geolocated social network provided by the Twitter application programming interface (API) to automatically detect the abnormal events. The methodology we propose consists of using an unsupervised competitive learning algorithm (self-organizing map (SOM)) and a density-based clustering method (density-based spatial clustering of applications with noise (DBCSAN)) to identify and detect crowds. The second stage is to build the entropy model to determine whether the detected crowds fit into the daily pattern with reference to a spatio-temporal entropy model, or whether they should be considered as evidence that something unusual occurs in the city because of their number, size, location and time of day. To detect an abnormal event in the city, it is sufficient to determine the real entropy model and to compare it with the reference model. For the normal day, the reference model is constructed offline for each time interval. The obtained results confirm the effectiveness of our method used in the first stage (SOM and DBSCAN stage) to detect and identify clusters dynamically, and imitating human activity. These findings also clearly confirm the detection of special days in New York City (NYC), which proves the performance of our proposed model.

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de Berg, Mark, Ade Gunawan, and Marcel Roeloffzen. "Faster DBSCAN and HDBSCAN in Low-Dimensional Euclidean Spaces." International Journal of Computational Geometry & Applications 29, no. 01 (March 2019): 21–47. http://dx.doi.org/10.1142/s0218195919400028.

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We present a new algorithm for the widely used density-based clustering method dbscan. For a set of [Formula: see text] points in [Formula: see text] our algorithm computes the dbscan-clustering in [Formula: see text] time, irrespective of the scale parameter [Formula: see text] (and assuming the second parameter MinPts is set to a fixed constant, as is the case in practice). Experiments show that the new algorithm is not only fast in theory, but that a slightly simplified version is competitive in practice and much less sensitive to the choice of [Formula: see text] than the original dbscan algorithm. We also present an [Formula: see text] randomized algorithm for hdbscan in the plane — hdbscan is a hierarchical version of dbscan introduced recently — and we show how to compute an approximate version of hdbscan in near-linear time in any fixed dimension.

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Irfan, M., and A. Shakoor. "Structural and electrical properties of dodecylbenzene sulphonicacid doped polypyrrole/zirconium oxide composites." Revista Mexicana de Física 65, no. 6 Nov-Dec (October 31, 2019): 607. http://dx.doi.org/10.31349/revmexfis.65.607.

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Polypyrrole (PPy) dispersed in an organic solvents were synthesized by means of dodecylbenzenesulphonic acid (DBSA) as useful dopant. Composites of doped PPy with DBSA and also mixed with zirconium oxide (ZrO2) nanoparticles were achieved by chemical polymerization route. Raman spectroscopy has been adopted to confirm the interaction between PPy-DBSA and ZrO2. The SEM also confirms the dual phase structure of platelet and egg shell in PPy-DBSA-ZrO2. Temperature dependant DC conductivity exhibited three dimensional variable ranges hopping (3D-VRH) model. Density of states, hopping length in addition to activation energy were calculated and was observe to be effected by increasing the weight ratio of ZrO2 into PPy-DBSA.

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Hossain, Md Zakir, Md Jakirul Islam, Md Waliur Rahman Miah, Jahid Hasan Rony, and Momotaz Begum. "Develop a dynamic DBSCAN algorithm for solving initial parameter selection problem of the DBSCAN algorithm." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 3 (September 1, 2021): 1602. http://dx.doi.org/10.11591/ijeecs.v23.i3.pp1602-1610.

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The amount of data has been increasing exponentially in every sector such as banking securities, healthcare, education, manufacturing, consumer-trade, transportation, and energy. Most of these data are noise, different in shapes, and outliers. In such cases, it is challenging to find the desired data clusters using conventional clustering algorithms. DBSCAN is a popular clustering algorithm which is widely used for noisy, arbitrary shape, and outlier data. However, its performance highly depends on the proper selection of cluster radius (Eps) and the minimum number of points (MinPts) that are required for forming clusters for the given dataset. In the case of real-world clustering problems, it is a difficult task to select the exact value of Eps and (MinPts) to perform the clustering on unknown datasets. To address these, this paper proposes a dynamic DBSCAN algorithm that calculates the suitable value for (Eps) and (MinPts) dynamically by which the clustering quality of the given problem will be increased. This paper evaluates the performance of the dynamic DBSCAN algorithm over seven challenging datasets. The experimental results confirm the effectiveness of the dynamic DBSCAN algorithm over the well-known clustering algorithms.

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Dywili, Nomxolisi R., Afroditi Ntziouni, Chinwe Ikpo, Miranda Ndipingwi, Ntuthuko W. Hlongwa, Anne Yonkeu, Milua Masikini, Konstantinos Kordatos, and Emmanuel I. Iwuoha. "Graphene Oxide Decorated Nanometal-Poly(Anilino-Dodecylbenzene Sulfonic Acid) for Application in High Performance Supercapacitors." Micromachines 10, no. 2 (February 11, 2019): 115. http://dx.doi.org/10.3390/mi10020115.

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Graphene oxide (GO) decorated with silver (Ag), copper (Cu) or platinum (Pt) nanoparticles that are anchored on dodecylbenzene sulfonic acid (DBSA)-doped polyaniline (PANI) were prepared by a simple one-step method and applied as novel materials for high performance supercapacitors. High-resolution transmission electron microscopy (HRTEM) and high-resolution scanning electron microscopy (HRSEM) analyses revealed that a metal-decorated polymer matrix is embedded within the GO sheet. This caused the M/DBSA–PANI (M = Ag, Cu or Pt) particles to adsorb on the surface of the GO sheets, appearing as aggregated dark regions in the HRSEM images. The Fourier transform infrared (FTIR) spectroscopy studies revealed that GO was successfully produced and decorated with Ag, Cu or Pt nanoparticles anchored on DBSA–PANI. This was confirmed by the appearance of the GO signature epoxy C–O vibration band at 1040 cm−1 (which decreased upon the introduction of metal nanoparticle) and the PANI characteristic N–H stretching vibration band at 3144 cm−1 present only in the GO/M/DBSA–PANI systems. The composites were tested for their suitability as supercapacitor materials; and specific capacitance values of 206.4, 192.8 and 227.2 F·g−1 were determined for GO/Ag/DBSA–PANI, GO/Cu/DBSA–PANI and GO/Pt/DBSA–PANI, respectively. The GO/Pt/DBSA–PANI electrode exhibited the best specific capacitance value of the three electrodes and also had twice the specific capacitance value reported for Graphene/MnO2//ACN (113.5 F·g−1). This makes GO/Pt/DBSA–PANI a very promising organic supercapacitor material.

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Beecher, Suzanne M., Kevin C. Cahill, and Christoph Theopold. "Pedicled sural flaps versus free anterolateral thigh flaps in reconstruction of dorsal foot and ankle defects in children: a systematic review." Archives of Plastic Surgery 48, no. 4 (July 15, 2021): 410–16. http://dx.doi.org/10.5999/aps.2020.00983.

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Background This systematic review compared free anterolateral thigh (ALT) flaps versus pedicled distally based sural artery (DBSA) flaps for reconstruction of soft tissue defects of dorsal foot and ankle in children.Methods A systematic literature search was performed to identify cases where an ALT or DBSA was used to reconstruct the dorsal foot in children. A total of 19 articles were included in the systematic review.Results Eighty-three patients underwent an ALT reconstruction and 138 patients underwent a DBSA reconstruction. Patients who had a DBSA were more likely to require grafting of the donor site (P<0.001). The size of ALT flaps was significantly larger than DBSA flaps (P=0.002). Subsequent flap thinning was required in 30% of patients after ALT and 12% of patients after DBSA reconstruction (P<0.001). Complications occurred in 11.6% of DBSA and 8.4% of ALT flaps (8.4%).Conclusions Both flaps are valid options in reconstructing pediatric foot and ankle defects. Each flap has advantages and disadvantages as discussed in this review article. In general for larger defects, an ALT flap was used. Flap choice should be based on the size of the defect.

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Journal articles on the topic 'Dbsan'

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