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Proceedings Paper

Research on self-organizing clustering of spatial points
Author(s): Limin Jiao; Yaolin Liu
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Paper Abstract

This paper studies the principle, method and application of spatial points clustering based on self-organizing neural networks. In this paper, we put forward a kind of composite clustering statistic, called generalized Euclidean distance, which is calculated by both geometric and semantic characters of spatial points. We propose the algorithm of spatial points clustering based on self-organizing feature map and generalized Euclidean distance. The clustering method in this paper can generate better result reflecting the clustering characters of spatial points. Finally, we employ a case study to probe into data classifying, gross error detecting and homogeneous areas partitioning using self-organizing spatial clustering result.

Paper Details

Date Published: 1 August 2007
PDF: 8 pages
Proc. SPIE 6751, Geoinformatics 2007: Cartographic Theory and Models, 67510I (1 August 2007); doi: 10.1117/12.759520
Show Author Affiliations
Limin Jiao, Wuhan Univ. (China)
Yaolin Liu, Wuhan Univ. (China)

Published in SPIE Proceedings Vol. 6751:
Geoinformatics 2007: Cartographic Theory and Models
Manchun Li; Jiechen Wang, Editor(s)

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