I-DBSCAN Algorithm with PSO for Density Based Clustering

Maritime anomaly detection can improve the situational awareness of vessel traffic supervisors and reduce maritime accidents. In order to better detect anomalous behaviour of a vessel in real time, a method that consists of a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and a recurrent neural network is presented. In the method presented, the parameters of the DBSCAN algorithm were determined through statistical analysis, and the results of clustering were taken as the traffic patterns to train a recurrent neural network composed of Long Short-Term Memory (LSTM) units. The neural network was applied as a vessel trajectory predictor to conduct real-time maritime anomaly detection. Based on data from the Chinese Zhoushan Islands, experiments verified the applicability of the proposed method. The results show that the proposed method can detect anomalous behaviours of a vessel regarding speed, course and route quickly.

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VISUALIZATION MODULE OF DENSITY-BASED CLUSTERING FOR HOTSPOT DISTRIBUTION IN INDONESIA USING MAPSERVER

Journal of Tropical Silviculture ◽

10.29244/j-siltrop.7.3.s58-s60 ◽

2016 ◽

Vol 7 (3) ◽

pp. S58-S60

Author(s):

Yenni Puspitasari ◽

Imas Sukaesih Sitanggang ◽

Rina Trisminingsih

Keyword(s):

Data Mining ◽

Information System ◽

Geographic Information System ◽

Geographic Information ◽

Mining Method ◽

Hotspot Analysis ◽

Web Based ◽

Dbscan Algorithm ◽

Density Based Clustering ◽

Minimum Points

A web-based Geographic Information System (GIS) has been built by previous researchers to visualize hotspots data in Indonesia. That GIS still has not contained a hotspot analysis module. Data mining method can be used to analyze hotspot data. This research aims to develop and to integrate a clustering module of hotspot in GIS which has been developed in the previous research. The clustering module for grouping hotspot data was built using the DBSCAN algorithm with PHP programming language. Clustering hotspot data was performed based on year, month, and province. Clustering parameters are epsilon and minimum points (MinPts). Epsilon value that used ranged from 0.01 to 0.1 while MinPts ranges from 1 to 6. The clustering results are shown in form of table which consists of the attribute Province, Regency, Latitude, Longitude and Cluster. Cluster column is the final result of clustering using algorithm DBSCAN. The attribute cluster represents clusters are visualized using the map of Indonesia that was built using MapServer. Visualization can help parties involved in making effective and efficient decisions to prevent forest fires.Key words: clustering, DBSCAN algorithm, geographic information system, hotspot

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Quantum algorithm for MMNG-based DBSCAN

Scientific Reports ◽

10.1038/s41598-021-95156-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Xuming Xie ◽

Longzhen Duan ◽

Taorong Qiu ◽

Junru Li

Keyword(s):

Domain Knowledge ◽

Clustering Algorithm ◽

Nearest Neighbor ◽

Local Density ◽

Quantum Algorithm ◽

Neighbor Graph ◽

Dbscan Algorithm ◽

Density Based Clustering ◽

Input Parameters ◽

Nearest Neighbor Graph

AbstractDBSCAN is a famous density-based clustering algorithm that can discover clusters with arbitrary shapes without the minimal requirements of domain knowledge to determine the input parameters. However, DBSCAN is not suitable for databases with different local-density clusters and is also a very time-consuming clustering algorithm. In this paper, we present a quantum mutual MinPts-nearest neighbor graph (MMNG)-based DBSCAN algorithm. The proposed algorithm performs better on databases with different local-density clusters. Furthermore, the proposed algorithm has a dramatic increase in speed compared to its classic counterpart.

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Faster DBSCAN and HDBSCAN in Low-Dimensional Euclidean Spaces

International Journal of Computational Geometry & Applications ◽

10.1142/s0218195919400028 ◽

2019 ◽

Vol 29 (01) ◽

pp. 21-47

Author(s):

Mark de Berg ◽

Ade Gunawan ◽

Marcel Roeloffzen

Keyword(s):

Scale Parameter ◽

Linear Time ◽

Randomized Algorithm ◽

Dbscan Clustering ◽

Dbscan Algorithm ◽

Fixed Dimension ◽

Density Based Clustering ◽

Fixed Constant ◽

Low Dimensional ◽

Approximate Version

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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NS-DBSCAN: A Density-Based Clustering Algorithm in Network Space

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8050218 ◽

2019 ◽

Vol 8 (5) ◽

pp. 218 ◽

Cited By ~ 9

Author(s):

Tianfu Wang ◽

Chang Ren ◽

Yun Luo ◽

Jing Tian

Keyword(s):

Density Distribution ◽

Hierarchical Clustering ◽

Clustering Algorithm ◽

Spatial Clustering ◽

Hot Spot ◽

Spatial Data Mining ◽

Dbscan Algorithm ◽

Density Based Clustering ◽

Hierarchical Clustering Algorithm ◽

Network Space

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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A Vibration Method for Discovering Density Varied Clusters

ISRN Artificial Intelligence ◽

10.5402/2012/723516 ◽

2012 ◽

Vol 2012 ◽

pp. 1-8 ◽

Cited By ~ 2

Author(s):

Mohammad T. Elbatta ◽

Raed M. Bolbol ◽

Wesam M. Ashour

Keyword(s):

Local Density ◽

Density Variation ◽

Clustering Method ◽

Number Of Clusters ◽

Vibration Method ◽

Correct Number ◽

The Core ◽

Dbscan Algorithm ◽

Density Based Clustering ◽

Different Shapes

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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A method for efficient clustering of spatial data in network space

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-202806 ◽

2021 ◽

pp. 1-18

Author(s):

Trang T.D. Nguyen ◽

Loan T.T. Nguyen ◽

Anh Nguyen ◽

Unil Yun ◽

Bay Vo

Keyword(s):

Spatial Data ◽

Euclidean Distance ◽

Clustering Algorithm ◽

Spatial Clustering ◽

Spatial Data Mining ◽

Spatial Data Analysis ◽

Clustering Methods ◽

Dbscan Algorithm ◽

Density Based Clustering ◽

Network Space

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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A Defense Mechanism Based on Improved Allocation Strategy of VMs Co-Resident Attack in Power Cloud Platform

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813666200311142532 ◽

2020 ◽

Vol 13 ◽

Author(s):

Yuancheng Li ◽

Pan Zhang ◽

Daoxing Li ◽

Jing Zeng

Keyword(s):

Virtual Machine ◽

Simulation Experiment ◽

Virtual Machines ◽

Defense Mechanism ◽

Security Threats ◽

Allocation Strategy ◽

Cloud Platform ◽

Deployment Strategy ◽

Dbscan Algorithm ◽

Maximum Minimum

Background: Cloud platform is widely used in electric power field. Virtual machine co-resident attack is one of the major security threats to the existing power cloud platform. Objective: This paper proposes a mechanism to defend virtual machine co-resident attack on power cloud platform. Method: Our defense mechanism uses the DBSCAN algorithm to classify and output the classification results through the random forest and uses improved virtual machine deployment strategy which combines the advantages of random round robin strategy and maximum/minimum resource strategy to deploy virtual machines. Results: we made a simulation experiment on power cloud platform of State Grid and verified the effectiveness of proposed defense deployment strategy. Conclusion: After the virtual machine deployment strategy is improved, the coverage of the virtual machine is remarkably reduced which proves that our defense mechanism achieves some effect of defending the virtual machine from virtual machine co-resident attack.

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