Argument free clustering for large spatial point-data sets via boundary extraction from Delaunay Diagram

Estivill‐Castro, Vladimir; Lee, Ickjai

doi:10.1016/s0198-9715(01)00044-8

Cited by 70 publications

(38 citation statements)

References 15 publications

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“…We used the AutoClust algorithm [32] for this purpose, which is a points clustering method with Delaunay triangulation. As for a Delaunay triangulation made of point set P, no point in P is inside the circumcircle of angle triangle in the Delaunay triangulation.…”

Section: Initial Clusteringmentioning

confidence: 99%

Extraction of Urban Power Lines from Vehicle-Borne LiDAR Data

et al. 2014

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Airborne LiDAR has been traditionally used for power line cruising. Nevertheless, data acquisition with airborne LiDAR is constrained by the complex environments in urban areas as well as the multiple parallel line structures on the same power line tower, which means it is not directly applicable to the extraction of urban power lines. Vehicle-borne LiDAR system has its advantages upon airborne LiDAR and this paper tries to utilize vehicle-borne LiDAR data for the extraction of urban power lines. First, power line points are extracted using a voxel-based hierarchical method in which geometric features of each voxel are calculated. Then, a bottom-up method for filtering the power lines belonging to each power line is proposed. The initial clustering and clustering recovery procedures are conducted iteratively to identify each power line. The final experiment demonstrates the high precision of this technique.

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Section: Initial Clusteringmentioning

confidence: 99%

Extraction of Urban Power Lines from Vehicle-Borne LiDAR Data

et al. 2014

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“…To determine which types of spatial patterns these sub-graphs are, in the following an indicator will be defined that considers the volumes of these sub-graphs. It should be pointed out that the previous multi-constraint Delaunay triangulation is mainly designed to detect various types of spatial clusters with different shapes and densities [25,26]. The proposed multi-constraint Delaunay triangulation in this study can give a more detailed analysis of the characteristics of edges from different levels, by which various spatial clusters and outliers can be simultaneously detected.…”

Section: Spatial Distribution Patterns Detectionmentioning

confidence: 96%

“…In order to detect spatial distribution patterns from spatial point events, a number of spatial clustering [25,26] and spatial outlier detection [27,28] methods have been proposed. However, these methods cannot accurately detect different types of spatial clusters and outliers simultaneously.…”

Section: Spatial Distribution Patterns Detectionmentioning

confidence: 99%

“…Delaunay triangulation has been proven to be an efficient tool for constructing spatial proximity relationships for spatial datasets and has thus been successfully employed in spatial clustering [25,26]. Unfortunately, for spatial point events multiple types of clusters and outliers may be involved, as described in Section 3.1 and existing methods are unable to accurately obtain these spatial patterns.…”

Section: Spatial Distribution Patterns Detectionmentioning

confidence: 99%

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A Framework for Discovering Evolving Domain Related Spatio-Temporal Patterns in Twitter

Shi

Deng

Yang

et al. 2016

IJGI

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Abstract:In massive Twitter datasets, tweets deriving from different domains, e.g., civil unrest, can be extracted to constitute spatio-temporal Twitter events for spatio-temporal distribution pattern detection. Existing algorithms generally employ scan statistics to detect spatio-temporal hotspots from Twitter events and do not consider the spatio-temporal evolving process of Twitter events. In this paper, a framework is proposed to discover evolving domain related spatio-temporal patterns from Twitter data. Given a target domain, a dynamic query expansion is employed to extract related tweets to form spatio-temporal Twitter events. The new spatial clustering approach proposed here is based on the use of multi-level constrained Delaunay triangulation to capture the spatial distribution patterns of Twitter events. An additional spatio-temporal clustering process is then performed to reveal spatio-temporal clusters and outliers that are evolving into spatial distribution patterns. Extensive experiments on Twitter datasets related to an outbreak of civil unrest in Mexico demonstrate the effectiveness and practicability of the new method. The proposed method will be helpful to accurately predict the spatio-temporal evolution process of Twitter events, which belongs to a deeper geographical analysis of spatio-temporal Big Data.

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“…The most commonly used spatial clustering algorithms are as follows: partitioning methods, such as K-Means [39]; hierarchical methods, such as CURE (Clustering Using Representatives) [40]; and density-based methods, such as DBSCAN (Density-based Spatial Clustering of Applications with Noise) [41]. In fact, no particular clustering method has been shown to be superior to its competitors with regards to all of the necessary aspects [42]. To date, the advantages and disadvantages of various algorithms have been extensively analyzed [43][44][45][46].…”

Section: Layout Optimization Of Refueling Service Based On Cluster Anmentioning

confidence: 99%

A Distance-Adaptive Refueling Recommendation Algorithm for Self-Driving Travel

Yang

Peng

et al. 2018

IJGI

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Abstract:Taking the maximum vehicle driving distance, the distances from gas stations, the route length, and the number of refueling gas stations as the decision conditions, recommendation rules and an early refueling service warning mechanism for gas stations along a self-driving travel route were constructed by using the algorithm presented in this research, based on the spatial clustering characteristics of gas stations and the urgency of refueling. Meanwhile, by combining ArcEngine and Matlab capabilities, a scenario simulation system of refueling for self-driving travel was developed by using c#.net in order to validate and test the accuracy and applicability of the algorithm. A total of nine testing schemes with four simulation scenarios were designed and executed using this algorithm, and all of the simulation results were consistent with expectations. The refueling recommendation algorithm proposed in this study can automatically adapt to changes in the route length of self-driving travel, the maximum driving distance of the vehicle, and the distance from gas stations, which could provide variable refueling recommendation strategies according to differing gas station layouts along the route. Therefore, the results of this study could provide a scientific reference for the reasonable planning and timely supply of vehicle refueling during self-driving travel.

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Argument free clustering for large spatial point-data sets via boundary extraction from Delaunay Diagram

Cited by 70 publications

References 15 publications

Extraction of Urban Power Lines from Vehicle-Borne LiDAR Data

Extraction of Urban Power Lines from Vehicle-Borne LiDAR Data

A Framework for Discovering Evolving Domain Related Spatio-Temporal Patterns in Twitter

A Distance-Adaptive Refueling Recommendation Algorithm for Self-Driving Travel

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