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Seriation using tree-penalized path length

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  • Aliyev, Denis A.
  • Zirbel, Craig L.

Abstract

Given a sample of n data points and an n by n dissimilarity matrix, data seriation methods produce a linear ordering of the objects, putting similar objects nearby in the ordering. One may visualize the reordered dissimilarity matrix with a heat map and thus understand the structure of the data, while still displaying the full matrix of dissimilarities. Good orderings produce heat maps that are easy to read and allow for clear interpretation.

Suggested Citation

  • Aliyev, Denis A. & Zirbel, Craig L., 2023. "Seriation using tree-penalized path length," European Journal of Operational Research, Elsevier, vol. 305(2), pages 617-629.
    • Handle: RePEc:eee:ejores:v:305:y:2023:i:2:p:617-629
    DOI: 10.1016/j.ejor.2022.06.026
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    References listed on IDEAS

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    1. Rego, César & Gamboa, Dorabela & Glover, Fred & Osterman, Colin, 2011. "Traveling salesman problem heuristics: Leading methods, implementations and latest advances," European Journal of Operational Research, Elsevier, vol. 211(3), pages 427-441, June.
    2. Hahsler, Michael, 2017. "An experimental comparison of seriation methods for one-mode two-way data," European Journal of Operational Research, Elsevier, vol. 257(1), pages 133-143.
    3. Hahsler, Michael & Hornik, Kurt & Buchta, Christian, 2008. "Getting Things in Order: An Introduction to the R Package seriation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i03).
    4. Hahsler, Michael & Hornik, Kurt, 2007. "TSPInfrastructure for the Traveling Salesperson Problem," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 23(i02).
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