Robust Clusterin of Large Geo-referenced Data Sets

Estivill‐Castro, Vladimir; Houle, Michael E.

doi:10.1007/3-540-48912-6_44

Cited by 18 publications

(15 citation statements)

References 16 publications

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“…Using the square of the Euclidean distance is typically motivated by considerations such as speeding the implementation by a constant factor and/or by an inductive principle of least-squares (as is the case with k-means (Estivill-Castro, 2000)). However, using squares of the Euclidean distance in the modeling makes the approach extremely sensitive to outliers and/or noise (Estivill-Castro and Houle, 1999;Murray and Estivill-Castro, 1998;Rousseeuw and Leroy, 1987). A second advantage of AUTOCLUST+ over COE is that COE needs to experiment with different values for the number of clusters.…”

Section: Discussionmentioning

confidence: 99%

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Clustering with obstacles for Geographical Data Mining

Estivill‐Castro

Lee

2004

ISPRS Journal of Photogrammetry and Remote Sensing

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Clustering algorithms typically use the Euclidean distance. However, spatial proximity is dependent on obstacles, caused by related information in other layers of the spatial database. We present a clustering algorithm suitable for large spatial databases with obstacles. The algorithm is free of user-supplied arguments and incorporates global and local variations. The algorithm detects clusters in complex scenarios and successfully supports association analysis between layers. All this occurs within O(n log n+[s + t] log n) expected time, where n is the number of points, s is the number of line segments that determine the obstacles and t is the number of Delaunay edges intersecting the obstacles. D

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Section: Discussionmentioning

confidence: 99%

“…Partitioning algorithms (Estivill-Castro and Houle, 1999;Ng and Han, 1994;Tung et al, 2000) provide limited opportunity for advancing the clustering to association analysis. They produce representatives of clusters that can be used to explore associations with other point data layers .…”

Section: Discussionmentioning

confidence: 99%

Clustering with obstacles for Geographical Data Mining

Estivill‐Castro

Lee

2004

ISPRS Journal of Photogrammetry and Remote Sensing

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“…Notable clustering algorithms following this paradigm are AUTOCLUST [11] and TRICLUST [12]. Other works ( [13], [14]) do not only take the pairwise distances information into account but also the inherent volumetric information carried by the d-simplices. We call forward-backward approaches these three-pass algorithms.…”

Section: A Dt-based Clustering Algorithmsmentioning

confidence: 99%

“…In particular, none of the cited Delaunay based algorithms take into account the amount of topological information carried by the simplices. The works presented in [13] and [14] address this problem as the former takes into account the distribution of the perimeters of the simplices and the latter proposes a composite measure based on the association of the perimeter, intra-simplex edge length distribution and local information. These two algorithms therefore refine the resulting graph by taking the information within the simplicial complex [15] into account: in opposition to graph theory-based techniques, they are simplicial complex-based ones.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Incremental Delaunay Triangulation Construction for Clustering

Razafindramanana

Rayar²,

Venturini³

2014

2014 22nd International Conference on Pattern Recognition

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In this paper, we propose an original solution to the problem of point cloud clustering. The proposed technique is based on a d-dimensional formulated Delaunay Triangulation (DT) construction algorithm and adapts it to the problem of cluster detection. The introduced algorithm allows this detection as along with the DT construction. Precisely, a criterion that detects occurrences of gaps in the simplex perimeter distribution is added during the incremental DT construction. This detection allows to label simplices as being inter-or intra cluster. Experimental results on 2D shape datasets are presented and discussed in terms of cluster detection and topological relationship preservation.

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“…If there are no common sides between beginning Voronoi region and obstacles, such as P 6 and P 2 , we can decide the direction from beginning Voronoi region to end as the searching direction. According to FOA, the Voronoi distance between P 6 to P 2 is 7.…”

Section: Spatial Voronoi Distance Of V-diagrammentioning

confidence: 99%

Voronoi diagram and spatial clustering in the presence of obstacles

Wang

Liu

et al. 2005

International Conference on Space Information Technology

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Clustering in spatial data mining is to group similar objects based on their distance, connectivity, or their relative density in space. Clustering algorithms typically use the Euclidean distance. In the real world, there exist many physical obstacles such as rivers, lakes and highways, and their presence may affect the result of clustering substantially. In this paper, we study the problem of clustering in the presence of obstacles and propose spatial clustering by Voronoi distance in Voronoi diagram (Thiessen polygon). Voronoi diagram has lateral spatial adjacency character. Based on it, we can express the spatial lateral adjacency relation conveniently and solve the problem derived from spatial clustering in the presence of obstacles. The method has three steps. First, building the Voronoi diagram in the presence of obstacles. Second, defining the Voronoi distance. Based on Voronoi diagram, we propose the Voronoi distance. Giving two spatial objects, P i and P j , The Voronoi distance is defined that the minimum object Voronoi regions number between P i and P j in the Voronoi diagram. Third, we propose Following-Obstacle-Algorithm (FOA). FOA includes three steps: the initializing step, the querying step and the pruning step. By FOA, we can get the Voronoi distance between any two objects. By Voronoi diagram and the FOA, the spatial clustering in the presence of obstacles can be accomplished conveniently, and more precisely. We conduct various performance studies to show that the method is both efficient and effective.

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Robust Clusterin of Large Geo-referenced Data Sets

Cited by 18 publications

References 16 publications

Clustering with obstacles for Geographical Data Mining

Clustering with obstacles for Geographical Data Mining

Incremental Delaunay Triangulation Construction for Clustering

Voronoi diagram and spatial clustering in the presence of obstacles

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