D-GridMST: clustering large distributed spatial databases
Edited book (chapter)
Chapter Title | D-GridMST: clustering large distributed spatial databases |
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Book Chapter Category | Edited book (chapter) |
ERA Publisher ID | 3337 |
Book Title | Classification and clustering for knowledge discovery |
Authors | Zhang, Ji (Author) and Liu, Han (Author) |
Editors | Halgamuge, Saman K. and Wang, Lipo |
Page Range | 61-72 |
Series | Studies in Computational Intelligence |
Number of Pages | 12 |
Year | 2005 |
Publisher | Springer |
Place of Publication | Berlin, Germany |
ISBN | 9783540260738 |
Digital Object Identifier (DOI) | https://doi.org/10.1007/11011620_5 |
Web Address (URL) | http://www.springerlink.com/content/dqj92ytagaj0pvfg/ |
Abstract | In this paper, we will propose a distributable clustering algorithm, called Distributed-GridMST (D-GridMST), which deals with large distributed spatial databases. D-GridMST employs the notions of multi-dimensional cube to partition the data space involved and uses density criteria to extract representative points from spatial databases, based on which a global MST of representatives is constructed. Such a MST is partitioned according to users clustering specification and used to label data points in the respective distributed spatial database thereafter. Since only the compact information of the distributed spatial databases is transferred via network, D-GridMST is characterized by small network transferring overhead. Experimental results show that D-GridMST is effective since it is able to produce exactly the same clustering result as that produced in centralized paradigm, making D-GridMST a promising tool for clustering large distributed spatial databases. |
Keywords | distributable clustering algorithms; distributed-GridMST; D-GridMST |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
401302. Geospatial information systems and geospatial data modelling | |
460605. Distributed systems and algorithms | |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | University of Toronto, Canada |
https://research.usq.edu.au/item/9z28z/d-gridmst-clustering-large-distributed-spatial-databases
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