A new method for hierarchical clustering combination

In the field of pattern recognition, combining different classifiers into a robust classifier is a common approach for improving classification accuracy. Recently, this trend has also been used to improve clustering performance especially in non-hierarchical clustering approaches. Generally hierarchical clustering is preferred in comparison with the partitional clustering for applications when the exact number of the clusters is not determined or when we are interested in finding the relation between clusters. To the best of our knowledge clustering combination methods proposed so far are based on partitional clustering and hierarchical clustering has been ignored. In this paper, a new method for combining hierarchical clustering is proposed. In this method, in the first step the primary hierarchical clustering dendrograms are converted to matrices. Then these matrices, which describe the dendrograms, are aggregated (using the matrix summation operator) into a final matrix with which the final clustering is formed. The effectiveness of different well known dendrogram descriptors and the one proposed by us for representing the dendrograms are evaluated and compared. The results show that all these descriptor work well and more accurate results (hierarchy of clusters) are obtained using hierarchical combination than combination of partitional clusterings.