Approximating Clustering of Fingerprint Vectors with Missing Values

Details Approximating Clustering of Fingerprint Vectors with Missing Values Approximating Clustering of Fingerprint Vectors with Missing Values

2005-11-23

arxiv.org/abs/cs/0511082v1

Computer Science

Data Structures and Algorithms

13 pages, 4 figures

Scientific paper

10.1007/s00453-008-9265-0

The problem of clustering fingerprint vectors is an interesting problem in Computational Biology that has been proposed in (Figureroa et al. 2004). In this paper we show some improvements in closing the gaps between the known lower bounds and upper bounds on the approximability of some variants of the biological problem. Namely we are able to prove that the problem is APX-hard even when each fingerprint contains only two unknown position. Moreover we have studied some variants of the orginal problem, and we give two 2-approximation algorithm for the IECMV and OECMV problems when the number of unknown entries for each vector is at most a constant.

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