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Based on the fact that for some countries a seven clusters solution was favoured by the algorithm, we fixed the number of clusters to seven in all countries.
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Then, the cluster algorithm was run 10 times to generate several clusters solutions that were next merged using the fuzzy clustering algorithm for a final robust cluster solution.
A three-cluster solution was retained.
We focus on a six cluster solution using this method.
We found that a two-cluster solution was most parsimonious.
Table 2 displays the centers from 15-cluster solution and a new center found in the 16-cluster solution.
Multiple discriminant analysis confirms the validity of the five-cluster solution.
Then, the clustering solution is found by applying the bisection method to the similarity matrix.
Finally, a new measure is designed to evaluate the effectiveness of clustering solution selection strategies.
In this paper, we focus on the clustering solution selection strategy in the cluster ensemble, and propose to view clustering solutions as features such that suitable feature selection techniques can be used to perform clustering solution selection.
In marketing, numerous other criteria impact on the selection of a cluster solution.
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