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Combinatorial Optimization Techniques for Network-Based Data Mining

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Handbook of Combinatorial Optimization

Abstract

The matters of discussion are combinatorial optimization aspects, concepts, and applications arising in the broad area of network-based data mining. The approach of representing real-world datasets as large-scale networks (graphs) has become increasingly popular during recent years. The purpose of this chapter is to briefly review the graph-theoretic and combinatorial optimization concepts that are important in the context of data mining, as well as to discuss the interpretation of these concepts from mathematical modeling perspective.

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Notes

  1. 1.

    These results are valid asymptotically almost surely (a.a.s.), which means that the probability that a given property takes place tends to 1 as the number of vertices N goes to infinity.

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Authors and Affiliations

  1. Industrial and Systems Engineering Department, University of Florida, 32611, Gainesville, FL, USA

    Oleg Shirokikh

  2. Industrial and Systems Engineering Department, University of Florida, 32611, Gainesville, FL, USA

    Vladimir Stozhkov

  3. Industrial and Systems Engineering Department, University of Florida, 32611, Shalimar, FL, USA

    Vladimir Boginski

Authors
  1. Oleg Shirokikh
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  2. Vladimir Stozhkov
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  3. Vladimir Boginski
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Corresponding author

Correspondence to Oleg Shirokikh .

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Editors and Affiliations

  1. Department of Industrial and Systems Eng, University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

  2. Department of Computer Science, University of Texas, Dallas, Richardson, Texas, USA

    Ding-Zhu Du

  3. Dept. Comp. Sci. & Engineering, University of California, San Diego, La Jolla, California, USA

    Ronald L. Graham

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Shirokikh, O., Stozhkov, V., Boginski, V. (2013). Combinatorial Optimization Techniques for Network-Based Data Mining. In: Pardalos, P., Du, DZ., Graham, R. (eds) Handbook of Combinatorial Optimization. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7997-1_6

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