Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/68268

Title

Efficiently matching proximity relationships in spatial databases

Author(s)

Lin, Xuemin;  Zhou, Xiaomei;  Liu, Chengfei

Abstract

Spatial data mining recently emerges from a number of real applications, such as real-estate marketing, urban planning, weather forecasting, medical image analysis, road traffic accident analysis, etc. It demands for efficient solutions for many new, expensive, and complicated problems. In this paper, we investigate a proximity matching problem among clusters and features. The investigation involves proximity relationship measurement between clusters and features. We measure proximity in an average fashion to address possible nonuniform data distribution in a cluster. An efficient algorithm, for solving the problem, is proposed and evaluated. The algorithm applies a standard multi-step paradigm in combining with novel lower and upper proximity bounds. The algorithm is implemented in several different modes. Our experiment results do not only give a comparison among them but also illustrate the efficiency of the algorithm.

Publication type

Conference paper

Source

Lecture notes in computer science: Advances in Spatial Databases: proceedings of the 6th International Symposium on Spatial Databases (SSD '99) Hong Kong, China, 20-23 July 1999 / Ralf Hartmut Guting, Dimitris Papadias and Fred Lochovsky (eds.), Vol. 1651, pp. 188-206

Publication year

1999

Keyword(s)

Data mining;  Proximity relationships;  Spatial query processing

Publisher

Springer-Verlag

ISSN

0302-9743 (series ISSN)

ISBN

9783540662471

Publisher URL

http://dx.doi.org/10.1007/3-540-48482-5_13

Copyright

Copyright © Springer-Verlag Berlin Heidelberg 1999.

Peer reviewed