P-Dbscan

Kisilevich, Slava; Mansmann, Florian; Keim, Daniel A.

doi:10.1145/1823854.1823897

Cited by 157 publications

(5 citation statements)

References 15 publications

(15 reference statements)

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“…( 1) clusters spatial and temporal data based on nonspatial spatial and temporal features; (2) This method assigns a density factor to each cluster to make noisy data in clusters that have different densities, unlike DBSCAN, detectable. The P-DBSCAN method [22] uses a series of labels to analyze the location of data. The main purpose of this method is to find locations with the help of a large number of photos and labels.…”

Section: Literature Reviewmentioning

confidence: 99%

Combination of Density-Based Spatial Clustering of Applications with Noise Method with Grid Search to Improve Complexity Using Nash Equilibrium

Kazemi,

Soleimani

2024

Preprint

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One of the important issues in data processing is clustering, the purpose of which is to find similar patterns in the data. Many clustering methods differ in their approaches and similarities. The density-based spatial clustering of applications with noise (DBSCAN) clustering method is one of the most practical density-based clustering methods that can identify training samples with different shapes, and for this reason, it has many applications in different fields. Although this method has its advantages, it has some weaknesses, such as the lack of proper performance in big data, the difficulty of determining Epsilons (Eps) and the Minimum number of points (Minpts) parameters for optimal clusters, etc. To solve these problems, in this paper, a dynamic method is used to solve the problem of identifying clusters with different densities, and another method is used to increase the speed of the algorithm and reduce the computational complexity. Testing the new method on several sets of data shows that the proposed method has a high efficiency in clustering and outperforms the density-based spatial clustering of applications with noise (DBSCAN) method in terms of complexity and efficiency.

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Section: Literature Reviewmentioning

confidence: 99%

Combination of Density-Based Spatial Clustering of Applications with Noise Method with Grid Search to Improve Complexity Using Nash Equilibrium

Kazemi,

Soleimani

2024

Preprint

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“…The latitude and longitude coordinates of 19 attractions were matched with the collected Flickr data as the area of interest (AOI) (see Figure 1). A variation of the density-based clustering algorithm (DBSCAN), P-DBSCAN proposed by Kisilevich, Mansmann and Keim (2010) was used to identify the 19 AOIs, where "P" stands for photo. In DBSCAN, AOIs are established by a spatial cluster analysis based on the number of photos in regions with close latitude and longitude coordinates.…”

Section: Vgi Data and The Spatial Weights Matrixmentioning

confidence: 99%

“…P-DBSCAN is an improved algorithm with the consideration of user numbers in the cluster analysis. As suggested by Kisilevich et al (2010), the searching radius of a photo for neighbouring photos is defined as 150 meters and the cut-off value of users in an AOI is 30.…”

Section: Vgi Data and The Spatial Weights Matrixmentioning

confidence: 99%

Visitor flow spillover effects on attraction demand: A spatial econometric model with multisource data

et al. 2022

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“…DBScan: DBScan is a density-based spatial clustering for data with noise [26]. It is a density-based clustering algorithm, because it works based on data density distribution points.…”

Section: Clustering Geographic Positionmentioning

confidence: 99%

Edge Computing Approach for Vessel Monitoring System

Ferreira

Martins

2019

Energies

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A vessel monitoring system (VMS) is responsible for real-time vessel movement tracking. At sea, most of the tracking systems use satellite communications, which have high associated costs. This leads to a less frequent transmission of data, which reduces the reliability of the vessel location. Our research work involves the creation of an edge computing approach on a local VMS, creating an intelligent process that decides whether the collected data needs to be transmitted or not. Only relevant data that can indicate abnormal behavior is transmitted. The remaining data is stored and transmitted only at ports when communication systems are available at lower prices. In this research, we apply this approach to a fishing control process increasing the data collection process from once every 10 min to once every 30 s, simultaneously decreasing the satellite communication costs, as only relevant data is transmitted in real-time to the competent central authorities. Findings show substantial communication savings from 70% to 90% as only abnormal vessel behavior is transmitted. Even with a data collection process of once every 30 s, findings also show that the use of more stable fishing techniques and fishing areas result in higher savings. The proposed approach is assessed as well in terms of the environmental impact of fishing and potential fraud detection and reduction.

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P-Dbscan

Cited by 157 publications

References 15 publications

Combination of Density-Based Spatial Clustering of Applications with Noise Method with Grid Search to Improve Complexity Using Nash Equilibrium

Combination of Density-Based Spatial Clustering of Applications with Noise Method with Grid Search to Improve Complexity Using Nash Equilibrium

Visitor flow spillover effects on attraction demand: A spatial econometric model with multisource data

Edge Computing Approach for Vessel Monitoring System

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