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Combinatorial Optimization in Data Mining

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Handbook of Combinatorial Optimization

Abstract

This chapter presents data mining techniques that are formulated as combinatorial optimization problems together with their applications. There are a number of cases where fundamental data mining tool is not combinatorial in nature, yet widely used special-purpose combinatorial extensions exist. For the sake of completeness, these fundamental tools are also discussed in detail before the extensions with underlying combinatorial optimization problems. A number of computationally challenging data mining algorithms that have non-convex formulations are also explored.

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Notes

  1. 1.

    ∗ This work is supported by University of Houston New Faculty Research Grant.

  2. 2.

    An exact equality for balancing constraint is likely to lead to infeasible solutions depending on the number of instances and ratios. Therefore, a subtle adjustment is usually necessary to ensure feasibility.

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Authors and Affiliations

  1. Department of Industrial Engineering, University of Houston, E209 Engineering Bldg. 2, 77204, Houston, TX, USA

    Samira Saedi

  2. Department of Industrial Engineering, University of Houston, E209 Engineering Bldg. 2, 77204, Houston, TX, USA

    O. Erhun Kundakcioglu

Authors
  1. Samira Saedi
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  2. O. Erhun Kundakcioglu
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Correspondence to Samira Saedi or O. Erhun Kundakcioglu .

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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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Saedi, S., Kundakcioglu, O.E. (2013). Combinatorial Optimization in 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_7

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