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Unformatted text preview: nt is member of exactly one cluster. One determines
the similarity between the clusters on the basis of this ﬁrst clustering and selects
the two clusters p, q of the clustering P with the minimum distance dist( p, q).
Both cluster are merged and one receives a new clustering. One continues this
procedure and re-calculates the distances between the new clusters in order to
join again the two clusters with the minimum distance dist( p, q). The algorithm
stops if only one cluster is remaining.
The distance can be computed according to Eq. 4. It is also possible to derive
the clusters directly on the basis of the similarity relationship given by a matrix.
For the computation of the similarity between clusters that contain more than
one element different distance measures for clusters can be used, e.g. based
Hierarchical Clustering Algorithms 40 LDV-FORUM A Brief Survey of Text Mining
on the outer cluster shape or the cluster center. Common linkage procedures
that make use of different cluster distance measures are single linkage, average
linkage or Ward’s procedure. The obtained clustering depends on the used
measure. Details can be found, for example,...
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This note was uploaded on 06/19/2011 for the course IT 2258 taught by Professor Aymenali during the Summer '11 term at Abu Dhabi University.
- Summer '11