Approximate clustering of incomplete fingerprints

Authors:
A. Figueroa;A. Goldstein;T. Jiang;M. Kurowski;A. Lingas;M. Persson
Affiliations:
Department of Computer Science, University of Texas-Pan American, TX, USA;Department of Mathematics, Yeshiva University, NY, USA;Computer Science Department, University of California, Riverside, CA, USA and Department of Computer Science, Tsinghua University, Beijing, China;Institute of Informatics, Warsaw University, Poland;Department of Computer Science, Lund University, Sweden;Department of Computer Science, Lund University, Sweden
Venue:
Journal of Discrete Algorithms
Year:
2008

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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+lnn,2+plnl) 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 2^2^p^-^1 and 2(1-12^2^p), respectively.