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Word Clustering with Validity Indices

  • Ahmad El Sayed
  • Julien Velcin
  • Djamel Zighed
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5032)

Abstract

The goal of any clustering algorithm producing flat partitions of data is to find the optimal clustering solution and the optimal number of clusters. One natural way to reach this goal without the need for parameters, is to involve a validity index in the clustering process, which can lead to an objective selection of the optimal number of clusters. In this paper, we provide two main contributions. Firstly, since validity indices have been mostly studied in small dimensional datasets, we have chosen to evaluate them in a real-world task: agglomerative clustering of words. Secondly, we propose a new context-aware method that aims at enhancing the validity indices usage as stopping criteria in agglomerative algorithms. Experimental results show that the method is a step-forward in using, with more reliability, validity indices as stopping criteria.

Keywords

Cluster Process Validity Index Relative Index Optimal Partition Agglomerative Cluster 
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 2008

Authors and Affiliations

  • Ahmad El Sayed
    • 1
  • Julien Velcin
    • 1
  • Djamel Zighed
    • 1
  1. 1.ERIC LaboratoryUniversity of Lyon 2 

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