k-divisive hierarchical clustering
Translated title
Author
Term
4. term
Education
Publication year
2003
Submitted on
2012-02-14
Abstract
In this master thesis, a novel divisive hierarchical clustering algorithm has been proposed. The algorithm is called the $k$-divisive hierarchical clustering algorithm. The aim of this algorithm is to overcome some of the disadvantages of the well-known divisive hierarchical clustering algorithms. The proposed algorithm builds the hierarchy by splitting a cluster into tow sub-clusters. The splitting process is performed by implementing the $k$-means algorithm, and the process of splitting a cluster is stopped by using two proposed stop criteria. The splitting process is halted so as to end up with a high quality dendogram. The algorithm together with the stop criteria are implemented and tested using artificial databases as well as real world databases.
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