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Restricted Randomness DBSCAN : A faster DBSCAN Algorithm
2021 Thirteenth International Conference on Contemporary Computing (IC3-2021), 2021Data Mining is the process of extracting useful and accurate information or patterns from large databases using different algorithms and methods of machine learning. To analyze the data, Clustering is one of the methods in which similar data is grouped together and DBSCAN clustering algorithm is the one, which is broadly used in numerous practical ...
Sashakt Pathak +3 more
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AutoRoC-DBSCAN: automatic tuning of DBSCAN to detect malicious DNS tunnels
Annals of Telecommunications, 2022International audience ; Modern attacks, such as advanced persistent threats, hide command-and-control channels inside authorized network traffic like DNS or DNS over HTTPS to infiltrate the local network and exfiltrate sensitive data. Detecting such malicious traffic using traditional techniques is cumbersome especially when the traffic encrypted like
Thi Quynh Nguyen +4 more
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2010 IEEE 18th Signal Processing and Communications Applications Conference, 2010
DBSCAN, which is the one of the density-based clustering methods in data mining, does the process of clustering, according to density of data. Although DBSCAN method seems effective in the small data sets, its efficiency in terms of processing time decreases with the growing of data volumes.
Tekbir, Mennan, Albayrak, Songül
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DBSCAN, which is the one of the density-based clustering methods in data mining, does the process of clustering, according to density of data. Although DBSCAN method seems effective in the small data sets, its efficiency in terms of processing time decreases with the growing of data volumes.
Tekbir, Mennan, Albayrak, Songül
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K-DBSCAN: An improved DBSCAN algorithm for big data
The Journal of Supercomputing, 2020Big data storage and processing are among the most important challenges now. Among data mining algorithms, DBSCAN is a common clustering method. One of the most important drawbacks of this algorithm is its low execution speed. This study aims to accelerate the DBSCAN execution speed so that the algorithm can respond to big datasets in an acceptable ...
Nahid Gholizadeh +2 more
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Proceedings of the 2018 International Conference on Management of Data, 2018
In most parallel DBSCAN algorithms, neighboring points are assigned to the same data partition for parallel processing to facilitate calculation of the density of the neighbors. This data partitioning scheme causes a few critical problems including load imbalance between data partitions, especially in a skewed data set.
Hwanjun Song, Jae-Gil Lee
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In most parallel DBSCAN algorithms, neighboring points are assigned to the same data partition for parallel processing to facilitate calculation of the density of the neighbors. This data partitioning scheme causes a few critical problems including load imbalance between data partitions, especially in a skewed data set.
Hwanjun Song, Jae-Gil Lee
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Comparison of DBSCAN and PCA-DBSCAN Algorithm for Grouping Earthquake Area
2021 International Congress of Advanced Technology and Engineering (ICOTEN), 2021Geologically, the territory of Indonesia is where the three active tectonic plates meet which are always moving and colliding with each other, resulting in earthquakes, volcanic pathways, and faults. Earthquake is a natural disaster that cannot be avoided or prevented, but the consequences of earthquakes can be minimized.
null Mustakim +5 more
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Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, 2015
DBSCAN is a popular method for clustering multi-dimensional objects. Just as notable as the method's vast success is the research community's quest for its efficient computation. The original KDD'96 paper claimed an algorithm with O(n log n) running time, where n is the number of objects.
Junhao Gan, Yufei Tao
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DBSCAN is a popular method for clustering multi-dimensional objects. Just as notable as the method's vast success is the research community's quest for its efficient computation. The original KDD'96 paper claimed an algorithm with O(n log n) running time, where n is the number of objects.
Junhao Gan, Yufei Tao
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SW-DBSCAN: A Grid-based DBSCAN Algorithm for Large Datasets
2020 6th International Conference on Web Research (ICWR), 2020Data clustering aims to discover the underlying structure of data. it has many applications in data analysis and it is one of the most widely used tools in data mining. DBSCAN is one of the most famous clustering algorithms. its advantages are to identify clusters of various shapes and define the number of clusters.
Negar Ohadi +5 more
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Multidimensional spatio-temporal change - DBSCAN (MDSTC-DBSCAN)
2023Die vorliegende Masterarbeit befasst sich mit der Anwendung von räumlich-zeitlichen Clustermethoden zur Abgrenzung von Submärkten auf der Basis von Immobilienkaufpreisen. Immobilienteilmärkte sind räumliche Gebiete, die in sich ähnliche Merkmale aufweisen, sich aber von anderen Submärkten unterscheiden.
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VDMR-DBSCAN: Varied Density MapReduce DBSCAN
2015DBSCAN is a well-known density based clustering algorithm, which can discover clusters of different shapes and sizes along with outliers. However, it suffers from major drawbacks like high computational cost, inability to find varied density clusters and dependency on user provided input density parameters.
Surbhi Bhardwaj, Subrat Kumar Dash
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