An adaptive spatial clustering algorithm based on delaunay triangulation

Deng, Min; Liu, Qiliang; Cheng, Tao; Shi, Yan

doi:10.1016/j.compenvurbsys.2011.02.003

Cited by 108 publications

(75 citation statements)

References 23 publications

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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: 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%

“…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%

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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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Section: Spatial Distribution Patterns Detectionmentioning

confidence: 99%

Section: Spatial Distribution Patterns Detectionmentioning

confidence: 99%

See 1 more Smart Citation

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

Shi

Deng

Yang

et al. 2016

IJGI

Self Cite

View full text Add to dashboard Cite

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“…On the other hand, both in the case of clustering of spatially referenced objects and point pattern analyses, it is suggested to use density-based algorithms [11,14], such as DBSCAN-like algorithms [11,15,16], DENCLUE [20], ASCDT [9] o DBSC [10], because of the same reason mentioned above.…”

Section: Proposed Solutionmentioning

confidence: 99%

“…This variety adds a level of complexity to the data mining task [3] and, because of that, new algorithms and methods for spatial clustering have been developed in recent years: REDCAP algorithms for regionalization; DBSCAN, NSCABDT, ASCDT, among others, for clustering of spatial points; and DBSCAN and RDBC for point patterns analyses, to name a few [3,[5][6][7][8][9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Knowledge Discovery Process for Description of Spatially Referenced Clusters

Rottoli

Merlino

García-Martínez

2017

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-Spatial clustering is an important field of spatial data mining and knowledge discovery that serves to partition a spatial data set to obtain disjoint subsets with spatial elements that are similar to each other. Existing algorithms can be used to perform three types of cluster analyses, including clustering of spatial points, regionalization and point pattern analysis. However, all these existing methods do not provide a description of the discovered spatial clusters, which is useful for decision making in many different fields. This work proposes a knowledge discovery process for the description of spatially referenced clusters that uses decision tree learning algorithms. Two proofs of concept of the proposed process using different spatial clustering algorithm on real data are also provided.

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