  Bagging and boosting are proved to be the best methods of building multiple
classifiers in classification combination problems. In the area of "flat
clustering" problems, it is also recognized that multi-clustering methods based
on boosting provide clusterings of an improved quality. In this paper, we
introduce a novel multi-clustering method for "hierarchical clusterings" based
on boosting theory, which creates a more stable hierarchical clustering of a
dataset. The proposed algorithm includes a boosting iteration in which a
bootstrap of samples is created by weighted random sampling of elements from
the original dataset. A hierarchical clustering algorithm is then applied to
selected subsample to build a dendrogram which describes the hierarchy.
Finally, dissimilarity description matrices of multiple dendrogram results are
combined to a consensus one, using a hierarchical-clustering-combination
approach. Experiments on real popular datasets show that boosted method
provides superior quality solutions compared to standard hierarchical
clustering methods.
