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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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