Clustering algorithms are unsupervised learning methods which explore and group similar patterns within a set of patterns. The goal of the clustering methods is to partition the set of input patterns into clusters such that all patterns within a cluster are similar to each other and different from the members of other clusters. In many applications, clustering methods which lead to representations that are hierarchical are more appropriate than flat representations. The most natural representation of a hierarchical clustering is its corresponding tree which is called dendrogram, which shows how the data points are grouped. Generally, hierarchical clustering is preferred in comparison with the nonhierarchical clustering for applications when the exact number of the clusters is not determined or when we are interested in finding the relation between clusters. In supervised pattern recognition, an effective method for solving complicated problems is to use decision combination. The idea of ensemble learning is to combine multiple learners’ predictions. In the area of supervised and unsupervised learning algorithms, ensembles often create better results compared to single solutions. justify; TEXT-INDENT: 18pt; MARGIN: 0cm 0cm 0pt; unicode-bidi: embed; DIRECTION: ltr; mso-layout-grid-align: none" Keywords: Ensemble clustering, hierarchical clustering, description matrix combination, boosting theory
