On the other hand some given classication can be seen

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Unformatted text preview: er algorithm coincides with the ideas of the user can be assessed by evaluation measures. A survey of different kinds of clustering algorithms and the resulting cluster types can be found in Steinbach et al. (2003). In the following, we first introduce standard evaluation methods and present then details for hierarchical clustering approaches, k-means, bi-section-k-means, self-organizing maps and the EM-algorithm. We will finish the clustering section with a short overview of other clustering approaches used for text clustering. 3.2.1 Evaluation of Clustering Results In general, there are two ways to evaluate clustering results. One the one hand statistical measures can be used to describe the properties of a clustering result. On the other hand some given classification can be seen as a kind of gold standard which is then typically used to compare the clustering results with the given classification. We discuss both aspects in the following. In the following, we first discuss measures which cannot make use of a given classification L of the documents. They are called indices in statistical literature and evaluate the quality of a clustering on the basis of statistic connections. One finds a large number of indices in literature (...
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