سال انتشار: ۱۳۹۰
محل انتشار: نوزدهمین کنفرانس مهندسی برق ایران
تعداد صفحات: ۴
Elaheh Rashedi – Isfahan University of Technology
Abdolreza Mirzaei –
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 on 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 hierarchicalclustering- combination approach. Experiments on real popular datasets show that boosted method provides superior quality solutions compared to standard hierarchical clustering methods.