Representable Hierarchical Clustering Methods for Asymmetric Networks

  • Gunnar Carlsson
  • Facundo MémoliEmail author
  • Alejandro Ribeiro
  • Santiago Segarra
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


This paper introduces the generative model of representability for hierarchical clustering methods in asymmetric networks, i.e., the possibility to describe a method through its action on a collection of networks called representers. We characterize the necessary and sufficient structural conditions needed on these representers in order to generate a method which is scale preserving and admissible with respect to two known axioms and, based on this result, we construct the family of cyclic clustering methods.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Gunnar Carlsson
    • 1
  • Facundo Mémoli
    • 2
    • 3
    Email author
  • Alejandro Ribeiro
    • 4
  • Santiago Segarra
    • 4
  1. 1.Department of MathematicsStanford UniversityStanfordUSA
  2. 2.Department of MathematicsThe Ohio State UniversityColumbusUSA
  3. 3.Department of Computer ScienceThe Ohio State UniversityColumbusUSA
  4. 4.Department of Electrical and Systems EngineeringUniversity of PennsylvaniaPhiladelphiaUSA

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