A sweep-line algorithm for spatial clustering

alik, Krista Rizman; alik, Borut

doi:10.1016/j.advengsoft.2008.06.003

Cited by 21 publications

(24 citation statements)

References 17 publications

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“…Each record of the frontier stores the key vertex index Pi and the index of the triangle Ti sharing its edge with the frontier (generating an adaptive threshold) in addition to the generated initial clustered index Ci. Fortunately, ASC will not have a projection-missed frontier, as previously mentioned in literature [41].…”

Section: Clusteringmentioning

confidence: 78%

“…The points are considered to be similar if the points are within a The algorithm proposed in this paper utilizes the Gestalt theory and the associated definition of the dynamic adaptive threshold. It can efficiently locate the adaptive clusters of arbitrary shapes and can acclimate to the uneven density characteristics of spatial data to avoid the requirements of preset global parameters, such as those necessary for DBSCAN, DENCLUE and other algorithms [41]. ASC works in a four-phase process: basic conceptualization, initialization, clustering and cluster merging.…”

Section: Asc Algorithmmentioning

confidence: 99%

“…• dist (pi,pl)≤Ɛ(t) and dist (pi,pr)≤Ɛ(t), where if pl and pr are members of the same cluster, before pi is placed into the same cluster. Otherwise, pi is a merging point between left and right clusters [41].…”

Section: Clusteringmentioning

confidence: 99%

“…We used a previously applied method [41] to merge the indices of the clusters (Figure 8b provides an example). In this example, the clusters Cl and Cr are merged via pi and the smallest index value is preserved.…”

Section: Merging Clustersmentioning

confidence: 99%

See 3 more Smart Citations

An Adaptive Sweep-Circle Spatial Clustering Algorithm Based on Gestalt

Zhan¹,

Deng²,

Zheng³

2017

Preprint

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An adaptive spatial clustering (ASC) algorithm is proposed in this present study, which employs sweep-circle techniques and a dynamic threshold setting based on the Gestalt theory to detect spatial clusters. The proposed algorithm can automatically discover clusters in one pass, rather than through the modification of the initial model (for example, a minimal spanning tree, Delaunay triangulation or Voronoi diagram). It can quickly identify arbitrarily-shaped clusters while adapting efficiently to non-homogeneous density characteristics of spatial data, without the need of prior knowledge or parameters. The proposed algorithm is also ideal for use in data streaming technology with dynamic characteristics flowing in the form of spatial clustering in large data sets.

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Section: Clusteringmentioning

confidence: 78%

Section: Asc Algorithmmentioning

confidence: 99%

Section: Clusteringmentioning

confidence: 99%

Section: Merging Clustersmentioning

confidence: 99%

See 2 more Smart Citations

An Adaptive Sweep-Circle Spatial Clustering Algorithm Based on Gestalt

Zhan¹,

Deng²,

Zheng³

2017

Preprint

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“…The characteristic of sweep line algorithms is that they reduce the computational complexity by translating an n dimensional static problem into an n − 1 dimensional dynamic problem. A clustering algorithm for spatial data which uses a sweep line algorithm is presented in [5], but it is not able to separate overlapping clusters, i.e. it can also not recognize intersecting hyperbola branches.…”

Section: Our Contributionmentioning

confidence: 99%

GamRec: A Clustering Method Using Geometrical Background Knowledge for GPR Data Preprocessing

Janning

Horváth

Busche

et al. 2012

IFIP Advances in Information and Communication Technology

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Abstract. GPR is a nondestructive method to scan the subsurface. On the resulting radargrams, originally interpreted manually in a time consuming process, one can see hyperbolas corresponding to buried objects. For accelerating the interpretation a machine shall be enabled to recognize hyperbolas on radargrams autonomously. One possibility is the combination of clustering with an expectation maximization algorithm. However, there is no suitable clustering algorithm for hyperbola recognition. Hence, we propose a clustering method specialized for this problem. Our approach is a directed shape based clustering combined with a sweep line algorithm. In contrast to other approaches our algorithm finds hyperbola shaped clusters and is (1) able to recognize intersecting hyperbolas, (2) noise robust and (3) does not require to know the number of clusters in the beginning but it finds this number. This is an important step towards the goal to fully automatize the buried object detection.

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