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Using Greedy algorithm: dbscan revisited II

Journal of Zhejiang University Science 2004年第11期5卷 1405-1412页

作者：岳士弘李平郭继东周水庚 InstituteofIndustrialProcessControl ZhejiangUniversityHangzhou310027China YiliTeacher＇sCollege Yining835000China

The density-based clustering algorithm presented is different from the classical Density-Based Spatial Clustering of Applications with Noise (dbscan) (Ester et al., 1996), and has the following advantages: first, Greedy algorithm substitutes for R*-tree (Bechmann et al., 1990) in dbscan to index the clustering space so that the clustering time cost is decreased to great extent and I/O memory load is reduced as well; second, the merging condition to approach to arbitrary-shaped clusters is designed carefully so that a single threshold can distinguish correctly all clusters in a large spatial dataset though some density-skewed clusters live in it. Finally, authors investigate a robotic navigation and test two artificial datasets by the proposed algorithm to verify its effectiveness and efficiency.

关键词： dbscan algorithm Greedy algorithm Density-skewed cluster

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Research on Bidirectional Matching algorithm of Variable Threshold SIFT Based on dbscan

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JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 2020年第5期53卷 222-228页

作者： Liu, Xue-jun Yuan, Bi-xian Wei, Yu-chen Yan, Yong Liu, Yong-xu Yang, Qi-si Qin, Jiao-jiao Beijing Inst Petrochem Technol 19 Qingyuan N Rd Beijing 102617 Peoples R China Beijing Univ Chem Technol North Third Ring Rd 15 Beijing 100029 Peoples R China

The safe distance measurement of stacking in hazardous chemicals warehouses is an important factor to ensure the safety of hazardous chemicals storage. Binocular vision distance measurement is a very promising technology. Aiming at the key problems in that feature points matching in distance measurement is easily affected by illumination and feature similarity, and based on the degree of density, the Density-Based Spatial Clustering of Applications with Noise (dbscan) clustering algorithm is proposed to realize Scale-invariant feature transform (SIFT) matching algorithm of variable thresholds in different regions of the image, and then adaptive neighborhood and bidirectional matching algorithms are used to further eliminate the mismatch points. The experimental results show that the matching rate of binocular distance measurement in hazardous chemicals warehouse is 68% and the accuracy of feature points is 98%.

关键词： Stacking Storage SIFT Detection algorithm dbscan algorithm Adaptive Neighborhood algorithm Bidirectional Matching

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Using Greedy algorithm: dbscan revisited II

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2004年第11期 94-101页

作者：岳士弘李平郭继东周水庚 Institute of Industrial Process Control Yining 835000 Hangzhou 310027 China Zhejiang University Yili Teacher’s College

The density-based clustering algorithm presented is different from the classical Density-Based Spatial Clus- tering of Applications with Noise (dbscan) (Ester et al., 1996), and has the following advantages: first, Greedy algorithm substitutes for R*-tree (Bechmann et al., 1990) in dbscan to index the clustering space so that the clustering time cost is decreased to great extent and I/O memory load is reduced as well; second, the merging condition to approach to arbi- trary-shaped clusters is designed carefully so that a single threshold can distinguish correctly all clusters in a large spatial dataset though some density-skewed clusters live in it. Finally, authors investigate a robotic navigation and test two artificial datasets by the proposed algorithm to verify its effectiveness and efficiency.

关键词： dbscan algorithm Greedy algorithm Density-skewed cluster

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Using Greedy algorithm:dbscan revisited Ⅱ

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浙江大学学报(英文版) 2004年第11期5卷 1405-1412页

作者：岳士弘李平郭继东周水庚 Institute of Industrial Process Control Zhejiang UniversityHangzhou 310027China Yili Teacher's College Yining 835000China

The density-based clustering algorithm presented is different from the classical Density-Based Spatial Clustering of Applications with Noise(dbscan)(Ester et al.,1996),and has the following advantages: first,Greedy algorithm substitutes for R*-tree(Bechmann et al.,1990)in dbscan to index the clustering space so that the clustering time cost is decreased to great extent and I/O memory load is reduced as well; second,the merging condition to approach to arbitrary-shaped clusters is designed carefully so that a single threshold can distinguish correctly all clusters in a large spatial dataset though some density-skewed clusters live in it. Finally,authors investigate a robotic navigation and test two artificial datasets by the proposed algorithm to verify its effectiveness and efficiency.

关键词： dbscan algorithm Greedy algorithm Density-skewed cluster

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Running space performance evaluation based on machine learning algorithms: A case study of the central districts of Kunming 24

Running space performance evaluation based on machine learni...

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Proceedings of the 2024 4th International Conference on Big Data, Artificial Intelligence and Risk Management

作者： Wenrui Zou Kunming University of Science and Technology Kunming Yunnan China

ISBN: (纸本)9798400709753

Amidst a national fitness initiative, urban running spaces in China often suffer from a lack of natural appeal, inadequate infrastructure, and insufficient vitality. To address these issues and offer precise recommendations for plateau lakeside cities, this paper investigates running spaces and the application of machine learning algorithms, focusing on Kunming's central urban area using spontaneously provided running track data. By incorporating the dbscan algorithm into the clustering analysis of running activities and spaces, we identified significant findings: 1. There are considerable spatial differences in running preferences in Kunming's central area, 2. The area has an excellent natural foundation, which is underutilized by runners, and 3. There are substantial disparities in the supply and demand for running spaces across different streets, indicating supply issues. These insights aim to provide a scientific basis for improving the supply relationship between running activities and spaces, contributing to the development of future healthy cities.

关键词： dbscan algorithm

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A New Clustering algorithm for Live Road Surveillance on Highways based on dbscan and Fuzzy Logic

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2020年第8期11卷 580-587页

作者： Alabbas, Hasanain Huszak, Arpad Budapest Univ Technol & Econ Dept Networked Syst & Serv H-1117 Budapest Hungary Al Qasim Green Univ Babylon Iraq

Video streaming over Vehicular Ad Hoc Networks is a promising technique (VANETs), and it has gained great importance in the last few years. The highly dynamic topology of VANETs makes high-quality video streaming very challenging. In order to provide the most useful camera views to the vehicles, clustering and cluster head selection techniques are used. Too frequent camera view changes can be annoying;therefore, we propose a new stable clustering algorithm to ensure a stable live road surveillance service without interruptions for vehicles that do not have enough vision area. In the proposed solution, we integrated Density-Based Spatial Clustering of Applications with Noise (dbscan) with Fuzzy Logic Control (FLC). dbscan is used to form the clusters, while FLC is used to find the best cluster head for the cluster. Different parameters are utilized like density parameters for dbscan, and relative speed, acceleration, leadership degree and vision area for fuzzy logic. Our proposed algorithm showed better results in terms of cluster lifetime and vehicle status change. Our proposed algorithm has been compared with another clustering scheme to prove the effectiveness of our proposed algorithm.

关键词： Vehicular ad hoc networks (VANETs) V2V intelligent transportation systems clustering algorithms road surveillance dbscan algorithm fuzzy logic control

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Comparison of wave-cluster and dbscan algorithms for landslide susceptibility assessment

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ENVIRONMENTAL EARTH SCIENCES 2021年第22期80卷 734-734页

作者： Mao, Yiming Mwakapesa, Deborah Simon Xu, Kaibin Lei, Chen Liu, Youcun Zhang, Maosheng Jiangxi Univ Sci & Technol Informat Engn Ganzhou 341000 Jiangxi Peoples R China MLR Key Lab Geohazards Loess Area 438 E Youyi Rd Xian 710054 Shaanxi Peoples R China

The aim of this study is to assess and compare two clustering algorithms, namely, the Wave-cluster and Density Based Spatial Clustering of Application with Noise (dbscan) algorithms, for landslide susceptibility assessment in Baota District, China. Based on historical reports, interpretation of aerial photographs and field survey reports, 293 were identified in the study area. Seven factors associated with landslide occurrence were prepared and used as inputs into the models for landslide susceptibility modelling. The models divided the study area grids into 478 and 465 subclasses, respectively, which were further grouped into five susceptibility levels using K-means algorithm, based on the landslide density values of the obtained subclasses. The resulting two models were validated and compared using area under the receiver operating characteristics (AUC-ROC) curve and statistical measures including sensitivity, specificity and accuracy. The analysis demonstrated that the Wave-cluster model outperformed the dbscan model with sensitivity, specificity and accuracy values of 88.05%, 89.2%, and 88.54%, respectively, while dbscan had sensitivity, specificity and accuracy values of 86.69%, 86.38%, and 86.56% respectively. In addition, the Wave-cluster model had higher value AUC of 0.823 than the dbscan model (0.808). This indicates that, the landslide susceptibility maps (LSMs) generated in this study had good performance for assessing landslide susceptibility in the study area and can be useful tools in assessing landslides, as the foundation for preventing and reducing landslide consequences for suitable social and economic development.

关键词： Landslide Landslide susceptibility Wave-cluster dbscan algorithm

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Parameter selection algorithm of dbscan based on K-means two classification algorithm

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JOURNAL OF ENGINEERING-JOE 2019年第23期2019卷 8676-8679页

作者： Chen, Shouhong Liu, Xinyu Ma, Jun Zhao, Shuang Hou, Xingna Guilin Univ Elect Technol Guilin 541000 Peoples R China Jiangsu Univ Zhenjiang 212013 Jiangsu Peoples R China

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.

关键词： data analysis unsupervised learning pattern clustering pattern classification human factors start critical value clustering dbscan algorithm neighbourhood radius experimental data analysis show minimum point number maximum distance value cluster centre-to-centre distance data points clustering centres noise density clustering algorithm unsupervised learning classification algorithm parameter selection algorithm

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DGBPSO-dbscan: An Optimized Clustering Technique Based on Supervised/Unsupervised Text Representation

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IEEE ACCESS 2024年 12卷 110798-110812页

作者： Abdulsahib, Asma Khazaal Balafar, M. A. Baradarani, Aryaz Univ Tabriz Fac Elect & Comp Engn Tabriz *** East Azerbaijan Iran Tessonics Inc Ctr Diagnost Imaging Res Windsor ON N9A Canada

Density-based spatial clustering of noisy applications (dbscan), a widely used density-based clustering technique, faces challenges in determining its key parameter, Eps, leading to manual specification and suboptimal clustering outcomes. Additionally, its time complexity poses limitations. This study introduces a novel approach to enhance the algorithm's performance. We combined the dbscan algorithm with another approach, the particle swarm optimization, based on a novel way to represent text, termed a dependency graph;this method is recognized as DGBPSO-dbscan (Dependency Graph Based Particle swarm algorithm for dbscan). This paper focuses on employing a novel approach for PSO variable upgrading to explore the Eps range more rapidly and effectively. This method offers a selection of informative elements from the text, where the initial groups are derived from the graph-based degree centrality, the PSO method is used to choose the most compelling features, and the dbscan algorithm is used for text clustering. The experimental findings indicate that the modified PSO is used to improve dbscan. We compared the outcomes of the suggested technique to those of the standard clustering algorithm. According to the assessment criteria MSE, accuracy, precision, recall, and F-measure, our technique has been demonstrated to be superior to the conventional method.

关键词： Clustering algorithms Reviews Particle swarm optimization Accuracy Partitioning algorithms Optimization Noise measurement Decision trees dbscan algorithm decision tree degree centrality dependency graph

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Vulnerability of Regional Aviation Networks Based on dbscan and Complex Networks

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Computer Systems Science & Engineering 2022年第11期43卷 643-655页

作者： Hang He Wanggen Liu Zhenhan Zhao Shan Heand Jinghui Zhang Civil Aviation Flight University of China Guanghan618307China Webchain Pty Ltd. SydneyNSW2118Australia

To enhance the accuracy of performance analysis of regional airline network,this study applies complex network theory and Density-Based Spatial Clustering of Applications with Noise(dbscan)algorithm to investigate the topology of regional airline network,constructs node importance index system,and clusters 161 airport nodes of regional airline ***,entropy power method and approximating ideal solution method(TOPSIS)is applied to comprehensively evaluate the importance of airport nodes and complete the classification of nodes and identification of key points;adopt network efficiency,maximum connectivity subgraph and network connectivity as vulnerability measurement indexes,and observe the changes of vulnerability indexes of key nodes under deliberate attacks and 137 nodes under random *** results demonstrate that the decreasing trend of the maximum connectivity subgraph indicator is slower and the decreasing trend of the network efficiency and connectivity indicators is faster when the critical nodes of the regional airline network are deliberately ***,the decreasing trend of the network efficiency indicator is faster and the decreasing trend of the maximum connectivity subgraph indicator is slower when the nodes of four different categories are randomly ***,it is proposed to identify and focus on protecting critical nodes in order to better improve the security level of regional airline system.

关键词： dbscan algorithm regional airlines key node vulnerability

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