D-GridMST: clustering large distributed spatial databases

Zhang, Ji and Liu, Han (2005) D-GridMST: clustering large distributed spatial databases. In: Classification and clustering for knowledge discovery. Studies in Computational Intelligence (4). Springer, Berlin, Germany, pp. 61-72. ISBN 978-3-540-26073-8

Preview	PDF (Accepted Version) Zhang_Liu_Book_Chpater_AV.pdf Download (770Kb)
Preview	PDF (Documentation) Binder1.pdf Download (245Kb)

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.

Statistics for this ePrint Item

Item Type:	Book Chapter (Commonwealth Reporting Category B)
Refereed:	Yes
Item Status:	Live Archive
Additional Information:	Chapter 5. Author's version deposited with blanket permission of publisher.
Faculty / Department / School:	Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited:	28 Sep 2009 00:24
Last Modified:	21 Sep 2016 01:41
Uncontrolled Keywords:	distributable clustering algorithms; distributed-GridMST; D-GridMST
Fields of Research :	08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining 09 Engineering > 0909 Geomatic Engineering > 090903 Geospatial Information Systems 08 Information and Computing Sciences > 0805 Distributed Computing > 080501 Distributed and Grid Systems
Socio-Economic Objective:	E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI:	10.1007/11011620_5
URI:	http://eprints.usq.edu.au/id/eprint/5634

Actions (login required)

Archive Repository Staff Only