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.
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El Sayed, A., Velcin, J., Zighed, D. (2008). Word Clustering with Validity Indices. In: Bergler, S. (eds) Advances in Artificial Intelligence. Canadian AI 2008. Lecture Notes in Computer Science(), vol 5032. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68825-9_25
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DOI: https://doi.org/10.1007/978-3-540-68825-9_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68821-1
Online ISBN: 978-3-540-68825-9
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