Approximate clustering of incomplete fingerprints

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Approximate clustering of incomplete fingerprints

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Publication Article, peer reviewed scientific
Title Approximate clustering of incomplete fingerprints
Author(s) Figueroa, Andres ; Goldstein, Avraham ; Jiang, Tao ; Kurowski, Maciej ; Lingas, Andrzej ; Persson, Mia
Date 2008
English abstract
We study the problem of clustering fingerprints with at most p missing values (CMV(p) for short) naturally arising in oligonucleotide fingerprinting, which is an efficient method for characterizing DNA clone libraries. We show that already CMV(2) is NP-hard. We also show that a greedy algorithm yields a min(1+ln n , 2+pln l) approximation for CMV(p), and can be implemented to run in O(nl2^p) time. We also introduce other variants of the problem of clustering incomplete fingerprints based on slightly different optimization criteria and show that they can be approximated in polynomial time with ratios 2^(2p−1) and 2(1-1/(2^(2p))), respectively.
DOI http://dx.doi.org/10.1016/j.jda.2007.01.004 (link to publisher's fulltext)
Publisher Elsevier
Host/Issue Journal of Discrete Algorithms;1
Volume 6
ISSN 1570-8667
Pages 103-108
Language eng (iso)
Subject(s) approximation algorithms
oligonucleotide fingerprinting
clustering
NP-hardness
Technology
Research Subject Categories::TECHNOLOGY
Handle http://hdl.handle.net/2043/16086 (link to this page)

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