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Author Kleinberg, Jon ♦ Papadimitriou, Christos ♦ Raghavan, Prabhakar
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2004
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Clustering ♦ Approximation algorithms ♦ Data mining ♦ Market segmentation
Abstract We study a novel genre of optimization problems, which we call segmentation problems, motivated in part by certain aspects of clustering and data mining. For any classical optimization problem, the corresponding segmentation problem seeks to partition a set of cost vectors into several $\textit{segments},$ so that the overall cost is optimized. We focus on two natural and interesting (but MAXSNP-complete) problems in this class, the hypercube segmentation problem and the catalog segmentation problem, and present approximation algorithms for them. We also present a general greedy scheme, which can be specialized to approximate any segmentation problem.
ISSN 00045411
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2004-03-01
Publisher Place New York
e-ISSN 1557735X
Journal Journal of the ACM (JACM)
Volume Number 51
Issue Number 2
Page Count 18
Starting Page 263
Ending Page 280

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Source: ACM Digital Library