NDLI: Spatial Clustering Algorithm Based on Optimized-Division

Content Provider	IEEE Xplore Digital Library
Author	Jian-pei Zhang Yue Yang Jing Yang Ze-bao Zhang Zhuo Liu
Copyright Year	2007
Description	Author affiliation: Harbin Eng. Univ., Harbin (Jian-pei Zhang; Yue Yang; Jing Yang; Ze-bao Zhang)
Abstract	Traditional grid-density based spatial clustering algorithms divide input data space into partitions with same width and neglect the natural distributing character of initial data space. A new robust spatial clustering algorithm based on optimized-division (OpD-Clus) is proposed in this paper. Spatial data space is divided by hyper-planes which are encertained with axis-paralleled histogram in OpD-Clus algorithm. Division of data space relies on natural distributing character of input data space to improve the accuracy and efficiency of spatial clustering. Simultaneously, the outstanding difference between density-region and spare-region makes setting of density threshold parameter easily and reduces the parameter dependence of spatial clustering algorithm. The validity, efficiency and un-sensitivity of parameters of OpD-Clus algorithm is demonstrated by experiment results.
Starting Page	266
Ending Page	271
File Size	1112016
Page Count	6
File Format	PDF
ISBN	9780769528748
DOI	10.1109/FSKD.2007.525
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Histograms Space technology Scalability Clustering algorithms Optimization methods Data engineering Educational institutions Partitioning algorithms Data mining
Content Type	Text
Resource Type	Article

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