Watersheds on Hypergraphs for Data Clustering

  • Fabio Dias
  • Moussa R. Mansour
  • Paola Valdivia
  • Jean Cousty
  • Laurent Najman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10225)

Abstract

We present a novel extension of watershed cuts to hypergraphs, allowing the clustering of data represented as an hypergraph, in the context of data sciences. Contrarily to the methods in the literature, instances of data are not represented as nodes, but as edges of the hypergraph. The properties associated with each instance are used to define nodes and feature vectors associated to the edges. This rich representation is unexplored and leads to a data clustering algorithm that considers the induced topology and data similarity concomitantly. We illustrate the capabilities of our method considering a dataset of movies, demonstrating that knowledge from mathematical morphology can be used beyond image processing, for the visual analytics of network data. More results, the data, and the source code used in this work are available at https://github.com/015988/hypershed.

Keywords

Data clustering Hypergraphs Watershed algorithm 

Notes

Acknowledgments

Grants 2016/04391-2, 2014/12815-1, 2015/14426-5, 2013/21779-6, 2013/14089-3, 2011/22749-8 São Paulo Research Foundation (FAPESP). The views expressed are those of the authors and do not reflect the official policy or position of the São Paulo Research Foundation.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Fabio Dias
    • 1
  • Moussa R. Mansour
    • 2
    • 3
  • Paola Valdivia
    • 2
    • 4
  • Jean Cousty
    • 5
  • Laurent Najman
    • 5
  1. 1.Tandon School of EngineeringNew York UniversityNew YorkUSA
  2. 2.Instituto de Ciências Matemáticas e de ComputaçãoUniversidade de São PauloSão CarlosBrazil
  3. 3.Jack’s VenturesPerthAustralia
  4. 4.InriaSaclayFrance
  5. 5.Université Paris-Est, LIGM, Equipe A3SI, EsieeChamps-sur-MarneFrance

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