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Computing Medoids in Large Spatial Datasets
Geographic Data Mining and Knowledge Discovery
  • Kyriakos MOURATIDIS, Singapore Management University
  • Dimitris PAPADIAS, Hong Kong University of Science and Technology
  • Spiros PAPADIMITRIOU, IBM TJ Watson Research Center
Publication Type
Book Chapter
Version
acceptedVersion
Publication Date
1-2009
Abstract

In this chapter, we consider a class of queries that arise in spatial decision making and resource allocation applications. Assume that a company wants to open a number of warehouses in a city. Let P be the set of residential blocks in the city. P represents customer locations to be potentially served by the company. At the same time, P also comprises the candidate warehouse locations because the warehouses themselves must be opened in some residential blocks.

Keywords
  • Distance,
  • Artificial Intelligence,
  • Physical Geography
Editor
Harvey J. Miller and Han Jiawei
ISBN
9781420073980
Identifier
10.1201/9781420073980
Publisher
CRC Press
City or Country
Boca Raton, FL
Creative Commons License
Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International
Additional URL
https://doi.org/10.1201/9781420073980
Citation Information
Kyriakos MOURATIDIS, Dimitris PAPADIAS and Spiros PAPADIMITRIOU. "Computing Medoids in Large Spatial Datasets" 2nd ed.Geographic Data Mining and Knowledge Discovery (2009) p. 189 - 226
Available at: http://works.bepress.com/kyriakos_mouratidis/19/