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A New Clustering Approach for Symbolic Data and Its Validation: Application to the Healthcare Data

  • Haytham Elghazel
  • Véronique Deslandres
  • Mohand-Said Hacid
  • Alain Dussauchoy
  • Hamamache Kheddouci
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4203)

Abstract

Graph coloring is used to characterize some properties of graphs. A b-coloring of a graph G (using colors 1,2,...,k) is a coloring of the vertices of G such that (i) two neighbors have different colors (proper coloring) and (ii) for each color class there exists a dominating vertex which is adjacent to all other k-1 color classes. In this paper, based on a b-coloring of a graph, we propose a new clustering technique. Additionally, we provide a cluster validation algorithm. This algorithm aims at finding the optimal number of clusters by evaluating the property of color dominating vertex. We adopt this clustering technique for discovering a new typology of hospital stays in the French healthcare system.

Keywords

Cluster Algorithm Graph Coloring Diagnosis Related Group Symbolic Data Optimal Partition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Haytham Elghazel
    • 1
  • Véronique Deslandres
    • 1
  • Mohand-Said Hacid
    • 2
  • Alain Dussauchoy
    • 1
  • Hamamache Kheddouci
    • 1
  1. 1.PRISMa LaboratoryClaude Bernard University of Lyon IVilleurbanneFrance
  2. 2.LIRIS LaboratoryClaude Bernard University of Lyon IVilleurbanneFrance

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