Hierarchical constraints

  • Authors:
  • Korinna Bade;Andreas Nürnberger

  • Affiliations:
  • Department of Computer Science and Languages, Anhalt University of Applied Sciences, Köthen (Anhalt), Germany 06366;Fakultät für Informatik, Institut für Technische und Betriebliche Informationssysteme, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany 39106

  • Venue:
  • Machine Learning
  • Year:
  • 2014

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Abstract

Constrained clustering received a lot of attention in the last years. However, the widely used pairwise constraints are not generally applicable for hierarchical clustering, where the goal is to derive a cluster hierarchy instead of a flat partition. Therefore, we propose for the hierarchical setting--based on the ideas of pairwise constraints--the use of must-link-before (MLB) constraints. In this paper, we discuss their properties and present an algorithm that is able to create a hierarchy by considering these constraints directly. Furthermore, we propose an efficient data structure for its implementation and evaluate its effectiveness with different datasets in a text clustering scenario.