Computing Medoids in Large Spatial Datasets

• Main Authors: MOURATIDIS, Kyriakos, PAPADIAS, Dimitris, PAPADIMITRIOU, Spiros
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2009
• Subjects: Distance; Artificial Intelligence; Physical Geography; Databases and Information Systems; Geography; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/247; https://ink.library.smu.edu.sg/context/sis_research/article/1246/viewcontent/Computing_Medoids_in_Large_Spatial_Dataset_2009.pdf
• Institution: Singapore Management University

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.