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A Maximum Profit Coverage Algorithm with Application to Small Molecules Cluster Identification

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Experimental Algorithms (WEA 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4007))

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Abstract

In this paper we present the cluster identification of molecules (CIM), which is a clustering problem in a finite metric space. We model the problem as a parameter estimation via likelihood maximization and as a novel clustering problem, the maximum profit coverage problem (MPCP). We present a numerical study in which we compare a greedy heuristic and a random heuristic for MPCP, to the known Expectation Minimization approach for the likelihood maximization model. We present a polynomial time approximation scheme for MPCP in Euclidean space.

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Authors and Affiliations

  1. Department of Statistics and Operations Resea rch, Tel Aviv University, Tel Aviv, 69978, Israel

    Refael Hassin & Einat Or

Authors
  1. Refael Hassin
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  2. Einat Or
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Editor information

Editors and Affiliations

  1. Software Department, Edifici Omega, Universitat Politècnica de Catalunya, Campus Nord, Jordi Girona 1-3, 08034, Barcelona, Spain

    Carme Àlvarez

  2. ALBCOM Research Group, Universitat Politècnica de Catalunya, Edifici Ω, Campus Nord, Jordi Girona, 1-3, 08034, Barcelona, Spain

    María Serna

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© 2006 Springer-Verlag Berlin Heidelberg

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Hassin, R., Or, E. (2006). A Maximum Profit Coverage Algorithm with Application to Small Molecules Cluster Identification. In: Àlvarez, C., Serna, M. (eds) Experimental Algorithms. WEA 2006. Lecture Notes in Computer Science, vol 4007. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11764298_24

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  • DOI: https://doi.org/10.1007/11764298_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34597-8

  • Online ISBN: 978-3-540-34598-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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