Relevant bibliographies by topics / DBSCAN Method

Academic literature on the topic 'DBSCAN Method'

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Published: 30 May 2022
Last updated: 31 May 2022

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Journal articles on the topic "DBSCAN Method"

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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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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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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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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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Nguyen, Trang T. D., Loan T. T. Nguyen, Anh Nguyen, Unil Yun, and Bay Vo. "A method for efficient clustering of spatial data in network space." Journal of Intelligent & Fuzzy Systems 40, no. 6 (June 21, 2021): 11653–70. http://dx.doi.org/10.3233/jifs-202806.

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Spatial clustering is one of the main techniques for spatial data mining and spatial data analysis. However, existing spatial clustering methods primarily focus on points distributed in planar space with the Euclidean distance measurement. Recently, NS-DBSCAN has been developed to perform clustering of spatial point events in Network Space based on a well-known clustering algorithm, named Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The NS-DBSCAN algorithm has efficiently solved the problem of clustering network constrained spatial points. When compared to the NC_DT (Network-Constraint Delaunay Triangulation) clustering algorithm, the NS-DBSCAN algorithm efficiently solves the problem of clustering network constrained spatial points by visualizing the intrinsic clustering structure of spatial data by constructing density ordering charts. However, the main drawback of this algorithm is when the data are processed, objects that are not specifically categorized into types of clusters cannot be removed, which is undeniably a waste of time, particularly when the dataset is large. In an attempt to have this algorithm work with great efficiency, we thus recommend removing edges that are longer than the threshold and eliminating low-density points from the density ordering table when forming clusters and also take other effective techniques into consideration. In this paper, we develop a theorem to determine the maximum length of an edge in a road segment. Based on this theorem, an algorithm is proposed to greatly improve the performance of the density-based clustering algorithm in network space (NS-DBSCAN). Experiments using our proposed algorithm carried out in collaboration with Ho Chi Minh City, Vietnam yield the same results but shows an advantage of it over NS-DBSCAN in execution time.
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Li, J. W., X. Q. Han, J. W. Jiang, Y. Hu, and L. Liu. "AN EFFICIENT CLUSTERING METHOD FOR DBSCAN GEOGRAPHIC SPATIO-TEMPORAL LARGE DATA WITH IMPROVED PARAMETER OPTIMIZATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 581–84. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-581-2020.

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Abstract. How to establish an effective method of large data analysis of geographic space-time and quickly and accurately find the hidden value behind geographic information has become a current research focus. Researchers have found that clustering analysis methods in data mining field can well mine knowledge and information hidden in complex and massive spatio-temporal data, and density-based clustering is one of the most important clustering methods.However, the traditional DBSCAN clustering algorithm has some drawbacks which are difficult to overcome in parameter selection. For example, the two important parameters of Eps neighborhood and MinPts density need to be set artificially. If the clustering results are reasonable, the more suitable parameters can not be selected according to the guiding principles of parameter setting of traditional DBSCAN clustering algorithm. It can not produce accurate clustering results.To solve the problem of misclassification and density sparsity caused by unreasonable parameter selection in DBSCAN clustering algorithm. In this paper, a DBSCAN-based data efficient density clustering method with improved parameter optimization is proposed. Its evaluation index function (Optimal Distance) is obtained by cycling k-clustering in turn, and the optimal solution is selected. The optimal k-value in k-clustering is used to cluster samples. Through mathematical and physical analysis, we can determine the appropriate parameters of Eps and MinPts. Finally, we can get clustering results by DBSCAN clustering. Experiments show that this method can select parameters reasonably for DBSCAN clustering, which proves the superiority of the method described in this paper.
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Elbatta, Mohammad T., Raed M. Bolbol, and Wesam M. Ashour. "A Vibration Method for Discovering Density Varied Clusters." ISRN Artificial Intelligence 2012 (November 15, 2012): 1–8. http://dx.doi.org/10.5402/2012/723516.

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DBSCAN is a base algorithm for density-based clustering. It can find out the clusters of different shapes and sizes from a large amount of data, which is containing noise and outliers. However, it is fail to handle the local density variation that exists within the cluster. Thus, a good clustering method should allow a significant density variation within the cluster because, if we go for homogeneous clustering, a large number of smaller unimportant clusters may be generated. In this paper, an enhancement of DBSCAN algorithm is proposed, which detects the clusters of different shapes and sizes that differ in local density. Our proposed method VMDBSCAN first finds out the “core” of each cluster—clusters generated after applying DBSCAN. Then, it “vibrates” points toward the cluster that has the maximum influence on these points. Therefore, our proposed method can find the correct number of clusters.
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Chen, Hao-xuan, Fei Tao, Pei-long Ma, Li-na Gao, and Tong Zhou. "Applicability Evaluation of Several Spatial Clustering Methods in Spatiotemporal Data Mining of Floating Car Trajectory." ISPRS International Journal of Geo-Information 10, no. 3 (March 12, 2021): 161. http://dx.doi.org/10.3390/ijgi10030161.

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Spatial analysis is an important means of mining floating car trajectory information, and clustering method and density analysis are common methods among them. The choice of the clustering method affects the accuracy and time efficiency of the analysis results. Therefore, clarifying the principles and characteristics of each method is the primary prerequisite for problem solving. Taking four representative spatial analysis methods—KMeans, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Clustering by Fast Search and Find of Density Peaks (CFSFDP), and Kernel Density Estimation (KDE)—as examples, combined with the hotspot spatiotemporal mining problem of taxi trajectory, through quantitative analysis and experimental verification, it is found that DBSCAN and KDE algorithms have strong hotspot discovery capabilities, but the heat regions’ shape of DBSCAN is found to be relatively more robust. DBSCAN and CFSFDP can achieve high spatial accuracy in calculating the entrance and exit position of a Point of Interest (POI). KDE and DBSCAN are more suitable for the classification of heat index. When the dataset scale is similar, KMeans has the highest operating efficiency, while CFSFDP and KDE are inferior. This paper resolves to a certain extent the lack of scientific basis for selecting spatial analysis methods in current research. The conclusions drawn in this paper can provide technical support and act as a reference for the selection of methods to solve the taxi trajectory mining problem.
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Zhao, Jianghong, Yan Dong, Siyu Ma, Huajun Liu, Shuangfeng Wei, Ruiju Zhang, and Xi Chen. "An Automatic Density Clustering Segmentation Method for Laser Scanning Point Cloud Data of Buildings." Mathematical Problems in Engineering 2019 (July 7, 2019): 1–13. http://dx.doi.org/10.1155/2019/3026758.

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Segmentation is an important step in point cloud data feature extraction and three-dimensional modelling. Currently, it is also a challenging problem in point cloud processing. There are some disadvantages of the DBSCAN method, such as requiring the manual definition of parameters and low efficiency when it is used for large amounts of calculation. This paper proposes the AQ-DBSCAN algorithm, which is a density clustering segmentation method combined with Gaussian mapping. The algorithm improves upon the DBSCAN algorithm by solving the problem of automatic estimation of the parameter neighborhood radius. The improved algorithm can carry out density clustering processing quickly by reducing the amount of computation required.
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Dissertations / Theses on the topic "DBSCAN Method"

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Huo, Shiyin. "Detecting Self-Correlation of Nonlinear, Lognormal, Time-Series Data via DBSCAN Clustering Method, Using Stock Price Data as Example." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1321989426.

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Lundstedt, Magnus. "Implementation and Evaluation of Image Retrieval Method Utilizing Geographic Location Metadata." Thesis, Uppsala universitet, Teknisk-naturvetenskapliga fakulteten, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-171865.

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Multimedia retrieval systems are very important today with millions of content creators all over the world generating huge multimedia archives. Recent developments allows for content based image and video retrieval. These methods are often quite slow, especially if applied on a library of millions of media items. In this research a novel image retrieval method is proposed, which utilizes spatial metadata on images. By finding clusters of images based on their geographic location, the spatial metadata, and combining this information with existing content- based image retrieval algorithms, the proposed method enables efficient presentation of high quality image retrieval results to system users. Clustering methods considered include Vector Quantization, Vector Quantization LBG and DBSCAN. Clustering was performed on three different similarity measures; spatial metadata, histogram similarity or texture similarity. For histogram similarity there are many different distance metrics to use when comparing histograms. Euclidean, Quadratic Form and Earth Mover’s Distance was studied. As well as three different color spaces; RGB, HSV and CIE Lab.
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Faccioli, Caterina. "Spatial analysis in pathomics: a network based method applied on fluorescence microscopy." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25122/.

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Recently, some applications of spatial statistics in histopathology have been explored, also thanks to the development of innovative digital imaging techniques and machine learning algorithms. However, it seems that in all these studies only global spatial measures are considered, usually analysed in combination with other techniques that depart from spatial statistics. In this thesis we developed a new spatial statistic based method for histopathological image analysis, which exploits local spatial features derived from coordinates in space and area of the cells. This features are mostly based on reciprocal distance between cells and also includes network-related measures. The dataset we analysed consisted of many sections of lymphoid tissue, for which also fluorescence measures obtained with a particular multiplexing technique were available. We performed clustering on these fluorescence features in order to obtain some reference labels for our points. Then we applied a supervised learning algorithm in order to predict fluorescence labels from the spatial features. We measured the performance of our predictions by computing the difference between the accuracy of the classifier we applied and of a random classifier. What we obtained is that the accuracy score of our classifier was greater than the one of the dummy classifier in every image. From a qualitative point of view, by comparing the achieved predictions and the clustering of fluorescence features of our images we obtained good results (verified by a senior histopathologist), often managing to identify the zone around the germinal centres of the lymph nodes and other structures. We consider these results encouraging, since they prove the predictive capability of our spatial features towards biological structures. The potential of this work is big: these features could strongly enhance the results obtainable from fluorescence imaging, allowing to resolve previously undistinguishable structures.
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Arce, Munoz Samuel. "Optimized 3D Reconstruction for Infrastructure Inspection with Automated Structure from Motion and Machine Learning Methods." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8469.

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Infrastructure monitoring is being transformed by the advancements on remote sensing, unmanned vehicles and information technology. The wide interaction among these fields and the availability of reliable commercial technology are helping pioneer intelligent inspection methods based on digital 3D models. Commercially available Unmanned Aerial Vehicles (UAVs) have been used to create 3D photogrammetric models of industrial equipment. However, the level of automation of these missions remains low. Limited flight time, wireless transfer of large files and the lack of algorithms to guide a UAV through unknown environments are some of the factors that constraint fully automated UAV inspections. This work demonstrates the use of unsupervised Machine Learning methods to develop an algorithm capable of constructing a 3D model of an unknown environment in an autonomous iterative way. The capabilities of this novel approach are tested in a field study, where a municipal water tank is mapped to a level of resolution comparable to that of manual missions by experienced engineers but using $63\%$ . The iterative approach also shows improvements in autonomy and model coverage when compared to reproducible automated flights. Additionally, the use of this algorithm for different terrains is explored through simulation software, exposing the effectiveness of the automated iterative approach in other applications.
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Hezoučký, Ladislav. "Nástroj pro shlukovou analýzu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237169.

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The master' s thesis deals with cluster data analysis. There are explained basic concepts and methods from this domain. Result of the thesis is Cluster analysis tool, in which are implemented methods K-Medoids and DBSCAN. Adjusted results on real data are compared with programs Rapid Miner and SAS Enterprise Miner.
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Hanna, Peter, and Erik Swartling. "Anomaly Detection in Time Series Data using Unsupervised Machine Learning Methods: A Clustering-Based Approach." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273630.

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For many companies in the manufacturing industry, attempts to find damages in their products is a vital process, especially during the production phase. Since applying different machine learning techniques can further aid the process of damage identification, it becomes a popular choice among companies to make use of these methods to enhance the production process even further. For some industries, damage identification can be heavily linked with anomaly detection of different measurements. In this thesis, the aim is to construct unsupervised machine learning models to identify anomalies on unlabeled measurements of pumps using high frequency sampled current and voltage time series data. The measurement can be split up into five different phases, namely the startup phase, three duty point phases and lastly the shutdown phase. The approach is based on clustering methods, where the main algorithms of use are the density-based algorithms DBSCAN and LOF. Dimensionality reduction techniques, such as feature extraction and feature selection, are applied to the data and after constructing the five models of each phase, it can be seen that the models identifies anomalies in the data set given.​
För flera företag i tillverkningsindustrin är felsökningar av produkter en fundamental uppgift i produktionsprocessen. Då användningen av olika maskininlärningsmetoder visar sig innehålla användbara tekniker för att hitta fel i produkter är dessa metoder ett populärt val bland företag som ytterligare vill förbättra produktionprocessen. För vissa industrier är feldetektering starkt kopplat till anomalidetektering av olika mätningar. I detta examensarbete är syftet att konstruera oövervakad maskininlärningsmodeller för att identifiera anomalier i tidsseriedata. Mer specifikt består datan av högfrekvent mätdata av pumpar via ström och spänningsmätningar. Mätningarna består av fem olika faser, nämligen uppstartsfasen, tre last-faser och fasen för avstängning. Maskinilärningsmetoderna är baserade på olika klustertekniker, och de metoderna som användes är DBSCAN och LOF algoritmerna. Dessutom tillämpades olika dimensionsreduktionstekniker och efter att ha konstruerat 5 olika modeller, alltså en för varje fas, kan det konstateras att modellerna lyckats identifiera anomalier i det givna datasetet.
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Bjurenfalk, Jonatan, and August Johnson. "Automated error matching system using machine learning and data clustering : Evaluating unsupervised learning methods for categorizing error types, capturing bugs, and detecting outliers." Thesis, Linköpings universitet, Programvara och system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177280.

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For large and complex software systems, it is a time-consuming process to manually inspect error logs produced from the test suites of such systems. Whether it is for identifyingabnormal faults, or finding bugs; it is a process that limits development progress, and requires experience. An automated solution for such processes could potentially lead to efficient fault identification and bug reporting, while also enabling developers to spend more time on improving system functionality. Three unsupervised clustering algorithms are evaluated for the task, HDBSCAN, DBSCAN, and X-Means. In addition, HDBSCAN, DBSCAN and an LSTM-based autoencoder are evaluated for outlier detection. The dataset consists of error logs produced from a robotic test system. These logs are cleaned and pre-processed using stopword removal, stemming, term frequency-inverse document frequency (tf-idf) and singular value decomposition (SVD). Two domain experts are tasked with evaluating the results produced from clustering and outlier detection. Results indicate that X-Means outperform the other clustering algorithms when tasked with automatically categorizing error types, and capturing bugs. Furthermore, none of the outlier detection methods yielded sufficient results. However, it was found that X-Means’s clusters with a size of one data point yielded an accurate representation of outliers occurring in the error log dataset. Conclusively, the domain experts deemed X-means to be a helpful tool for categorizing error types, capturing bugs, and detecting outliers.
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Tomešová, Tereza. "Autonomní jednokanálový deinterleaving." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-445470.

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This thesis deals with an autonomous single-channel deinterleaving. An autonomous single-channel deinterleaving is a separation of the received sequence of impulses from more than one emitter to sequences of impulses from one emitter without a human assistance. Methods used for deinterleaving could be divided into single-parameter and multiple-parameter methods according to the number of parameters used for separation. This thesis primarily deals with multi-parameter methods. As appropriate methods for an autonomous single-channel deinterleaving DBSCAN and variational bayes methods were chosen. Selected methods were adjusted for deinterleaving and implemented in programming language Python. Their efficiency is examined on simulated and real data.
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Shreepathi, Subrahmanya, Hung Van Hoang, and Rudolf Holze. "Corrosion Protection Performance and Spectroscopic Investigations of Soluble Conducting Polyaniline-Dodecylbenzenesulfonate Synthesized via Inverse Emulsion Procedure." Universitätsbibliothek Chemnitz, 2009. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-200900775.

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Corrosion protection performance of a completely soluble polyaniline-dodecylbenzenesulfonic acid salt (PANI-DBSA) on C45 steel has been studied with electrochemical impedance and potentiodynamic measurements. Chloroform is the most suitable solvent to process the pristine PANI-DBSA because of negligible interaction of the solvent with the polyaniline (PANI) backbone. An anodic shift in the corrosion potential (DeltaE=~70  mV), a decrease in the corrosion current and a significant increase in the charge transfer resistance indicate a significant anti-corrosion performance of the soluble PANI deposited on the protected steel surface. Corrosion protection follows the mechanism of formation of a passive oxide layer on the surface of C45 steel. In situ UV-Vis spectroscopy was used to investigate the differences in permeability of aqueous anions into PANI-DBSA. Preliminary results of electron diffraction studies show that PANI-DBSA possesses an orthorhombic type of crystal structure. An increase in the feed ratio of DBSA to aniline increases the tendency of aggregation of spherical particles of PANI obvious in transmission electron microscopy. PANI-DBSA slowly loses its electrochemical activity in acid free electrolyte without undergoing degradation.
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Liang-ChenYue and 岳良晨. "A Botnet Feature Extraction Method By Integrating Genetic And DBScan Algorithms." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/48492885224477580871.

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碩士
國立成功大學
電機工程學系專班
101
The advancing of internet technology enables more convenient communications among computers, but there are also many problems hiding under the convenience of the computer networking. Hackers invade user’s computers and implant virus in various ways like emails, messaging programs, and system bugs. In recent years, Botnet has become the most massive way of virus-spreading. Similar to flu virus transmission, it commands infected computer through Internet Relay Chat software (IRC) to intrude other bug-containing computers and convey virus on internet in a speed much faster than normal virus. In this study, we make analysis and comparison by employing the similarity of the behavioral characteristics of host systems on Botnet. Data of live Botnet behavior are collected. Characteristics data are calculated by analyzing several common types of behavioral characteristics on internet. By integrating Genetic Algorithm and Clustering Algorithm, Detection Rate and False Positive Rate are worked out to characterize the matching combination for the Botnet, which can be applied to make contrast with the behavior of other computers for detection of the same Bot.
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Book chapters on the topic "DBSCAN Method"

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Esmaelnejad, Jamshid, Jafar Habibi, and Soheil Hassas Yeganeh. "A Novel Method to Find Appropriate ε for DBSCAN." In Intelligent Information and Database Systems, 93–102. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12145-6_10.

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Yoon, Jin Uk, Byoungwook Kim, and Joon-Min Gil. "An Improved DBSCAN Method Considering Non-spatial Similarity by Using Min-Hash." In Advances in Computer Science and Ubiquitous Computing, 599–605. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9343-7_84.

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Li, Xiaoling, Juntao Li, and Tao Mu. "A Local Map Construction Method for SLAM Problem Based on DBSCAN Clustering Algorithm." In Communications in Computer and Information Science, 540–49. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3415-7_45.

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Jebari, Sihem, Abir Smiti, and Aymen Louati. "A New Fuzzy Clustering Method Based on FN-DBSCAN to Determine the Optimal Input Parameters." In Learning and Analytics in Intelligent Systems, 593–602. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36778-7_65.

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Tripathy, Sarita, and Laxman Sahoo. "Improved Method for Noise Detection by DBSCAN and Angle Based Outlier Factor in High Dimensional Datasets." In Lecture Notes in Electrical Engineering, 213–21. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8715-9_27.

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Zishan Ali, Syed, Monica Makhija, Daljeet Choudhary, and Hitesh Singh. "An Efficient and Adaptive Method for Collision Probability of Ships, Icebergs Using CNN and DBSCAN Clustering Algorithm." In Emerging Technologies in Computer Engineering: Microservices in Big Data Analytics, 20–33. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8300-7_3.

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Panahandeh, Ghazaleh, and Niklas Åkerblom. "Clustering Driving Destinations Using a Modified DBSCAN Algorithm with Locally-Defined Map-Based Thresholds." In Computational Methods and Models for Transport, 97–103. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54490-8_7.

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S, Umadevi, and NirmalaSugirthaRajini. "Dimensionality Reduction of Production Data Using PCA and DBSCAN Techniques." In Intelligent Systems and Computer Technology. IOS Press, 2020. http://dx.doi.org/10.3233/apc200184.

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Now a day’s data mining concepts are applied in various fields like medical, agriculture, production, etc. Creation of cluster is one of the major problems in data analysis process. Various clustering algorithms are used for data analysis purpose which depends upon the applications. DBSCAN is the famous method to create cluster. This article describes DBSCAN clustering concept applied on production database. The main objective of this research article is to collect and group the related data from large amount of data and remove the unwanted data. This clustering algorithm removes the unwanted attributes and groups the related data based upon density value.
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Lamere, Alicia Taylor. "Cluster Analysis in R With Big Data Applications." In Open Source Software for Statistical Analysis of Big Data, 111–36. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2768-9.ch004.

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This chapter discusses several popular clustering functions and open source software packages in R and their feasibility of use on larger datasets. These will include the kmeans() function, the pvclust package, and the DBSCAN (density-based spatial clustering of applications with noise) package, which implement K-means, hierarchical, and density-based clustering, respectively. Dimension reduction methods such as PCA (principle component analysis) and SVD (singular value decomposition), as well as the choice of distance measure, are explored as methods to improve the performance of hierarchical and model-based clustering methods on larger datasets. These methods are illustrated through an application to a dataset of RNA-sequencing expression data for cancer patients obtained from the Cancer Genome Atlas Kidney Clear Cell Carcinoma (TCGA-KIRC) data collection from The Cancer Imaging Archive (TCIA).
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Savaş, Cihan, Mehmet Samet Yıldız, Süleyman Eken, Cevat İkibaş, and Ahmet Sayar. "Clustering Earthquake Data." In Big Data and Knowledge Sharing in Virtual Organizations, 224–39. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7519-1.ch010.

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Seismology, which is a sub-branch of geophysics, is one of the fields in which data mining methods can be effectively applied. In this chapter, employing data mining techniques on multivariate seismic data, decomposition of non-spatial variable is done. Then k-means clustering, density-based spatial clustering of applications with noise (DBSCAN), and hierarchical tree clustering algorithms are applied on decomposed data, and then pattern analysis is conducted using spatial data on the resulted clusters. The conducted analysis suggests that the clustering results with spatial data is compatible with the reality and characteristic features of regions related to earthquakes can be determined as a result of modeling seismic data using clustering algorithms. The baseline metric reported is clustering times for varying size of inputs.
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Conference papers on the topic "DBSCAN Method"

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Bessrour, Malek, Zied Elouedi, and Eric Lefevre. "E-DBSCAN: An evidential version of the DBSCAN method." In 2020 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2020. http://dx.doi.org/10.1109/ssci47803.2020.9308578.

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Smiti, Abir, and Zied Eloudi. "Soft DBSCAN: Improving DBSCAN clustering method using fuzzy set theory." In 2013 6th International Conference on Human System Interactions (HSI). IEEE, 2013. http://dx.doi.org/10.1109/hsi.2013.6577851.

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Ma, Li, Lei Gu Bo Li, Sou yi Qiao, and Jin Wang. "G-DBSCAN: An Improved DBSCAN Clustering Method Based On Grid." In Advanced Software Engineering & Its Applications 2014. Science & Engineering Research Support soCiety, 2014. http://dx.doi.org/10.14257/astl.2014.74.05.

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Jebari, Sihem, Abir Smiti, and Aymen Louati. "AF-DBSCAN: An unsupervised Automatic Fuzzy Clustering method based on DBSCAN approach." In 2019 IEEE International Work Conference on Bioinspired Intelligence (IWOBI). IEEE, 2019. http://dx.doi.org/10.1109/iwobi47054.2019.9114411.

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Smiti, Abir, and Zied Elouedi. "DBSCAN-GM: An improved clustering method based on Gaussian Means and DBSCAN techniques." In 2012 IEEE 16th International Conference on Intelligent Engineering Systems (INES). IEEE, 2012. http://dx.doi.org/10.1109/ines.2012.6249802.

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Smiti, Abir, and Zied Elouedi. "Fuzzy density based clustering method: Soft DBSCAN-GM." In 2016 IEEE 8th International Conference on Intelligent Systems (IS). IEEE, 2016. http://dx.doi.org/10.1109/is.2016.7737459.

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Song, Jin-yu, Yi-ping Guo, and Bin Wang. "The Parameter Configuration Method of DBSCAN Clustering Algorithm." In 2018 5th International Conference on Systems and Informatics (ICSAI). IEEE, 2018. http://dx.doi.org/10.1109/icsai.2018.8599429.

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Ding, Hu, Fan Yang, and Mingyue Wang. "On Metric DBSCAN with Low Doubling Dimension." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/426.

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Abstract:
The density based clustering method Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a popular method for outlier recognition and has received tremendous attention from many different areas. A major issue of the original DBSCAN is that the time complexity could be as large as quadratic. Most of existing DBSCAN algorithms focus on developing efficient index structures to speed up the procedure in low-dimensional Euclidean space. However, the research of DBSCAN in high-dimensional Euclidean space or general metric spaces is still quite limited, to the best of our knowledge. In this paper, we consider the metric DBSCAN problem under the assumption that the inliers (excluding the outliers) have a low doubling dimension. We apply a novel randomized k-center clustering idea to reduce the complexity of range query, which is the most time consuming step in the whole DBSCAN procedure. Our proposed algorithms do not need to build any complicated data structures and are easy to implement in practice. The experimental results show that our algorithms can significantly outperform the existing DBSCAN algorithms in terms of running time.
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Sabo, Kristian, and Rudolf Scitovski. "Multiple Ellipse Detection by using RANSAC and DBSCAN Method." In 9th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0008879301290135.

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Ren, Hongda, and Noel N. Schulz. "An Improved DBSCAN Method for Self-sufficient Microgrid Design." In 2018 North American Power Symposium (NAPS). IEEE, 2018. http://dx.doi.org/10.1109/naps.2018.8600608.

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