Clustering Algorithm of Uncertain Data in Obstacle Space

In recent years, uncertain data is generated widely in location data due to the inaccuracy of measurement instruction or the data attributes itself. The existence of obstacles in space brings the new challenges to spatial uncertain data clustering. This paper proposes OBS-UK-means (obstacle uncertai...

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Vydáno v:Jisuanji Kexue yu Tansuo / Journal of Computer Science and Frontiers Ročník 6; číslo 12; s. 1087 - 1097
Hlavní autoři: Cao, Keyan, Wang, Guoren, Han, Donghong, Yuan, Ye, Hu, Yachao, Qi, Baolei
Médium: Journal Article
Jazyk:čínština
Vydáno: 01.12.2012
Témata:
Algorithms
Clustering
Clusters
Computational efficiency
Computing time
Obstacles
Pruning
Strategy
ISSN:1673-9418
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Shrnutí:In recent years, uncertain data is generated widely in location data due to the inaccuracy of measurement instruction or the data attributes itself. The existence of obstacles in space brings the new challenges to spatial uncertain data clustering. This paper proposes OBS-UK-means (obstacle uncertain K-means) algorithm to cluster uncertain data in obstacle space, and also proposes two pruning strategies based on R-tree and Voronoi diagram and the shortest distance area concept, that greatly reduces the calculations. Finally, the experiment demonstrates that the efficiency and accuracy of the OBS-UK-means algorithm, and the pruning approach can improve the efficiency of the clustering algorithm, meanwhile, it doesn't damage the cluster effectiveness.
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ISSN:1673-9418
DOI:10.3778/j.issn.1673-9418.2012.12.003