Computing Medoids in Large Spatial Datasets

Author

Kyriakos MOURATIDIS, Singapore Management UniversityFollow
Dimitris PAPADIAS, Hong Kong University of Science and Technology
Spiros PAPADIMITRIOU, IBM TJ Watson Research Center

Publication Type

Book Chapter

Version

acceptedVersion

Publication Date

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

Discipline

Databases and Information Systems | Geography | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

Geographic Data Mining and Knowledge Discovery

Editor

Harvey J. Miller and Han Jiawei

First Page

189

Last Page

226

ISBN

9781420073980

Identifier

10.1201/9781420073980

Edition

2nd ed.

Publisher

CRC Press

City or Country

Boca Raton, FL

Citation

MOURATIDIS, Kyriakos; PAPADIAS, Dimitris; and PAPADIMITRIOU, Spiros. Computing Medoids in Large Spatial Datasets. (2009). Geographic Data Mining and Knowledge Discovery. 189-226.
Available at: https://ink.library.smu.edu.sg/sis_research/247

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.

Additional URL

https://doi.org/10.1201/9781420073980