OWA-based linkage and the genie correction for hierarchical clustering

© 2017 IEEE. In this paper we thoroughly investigate various OWA-based linkages in hierarchical clustering on numerous benchmark data sets. The inspected setting generalizes the well-known single, complete, and average linkage schemes, among others. The incorporation of weights into the cluster merge procedure creates an opportunity to make use of experts' knowledge about a particular data domain so as to generate partitions of a given data set that better reflect the true underlying cluster structure. Moreover, we introduce a correction for the inequality of cluster size distribution - similar to the one proposed in our recently introduced Genie algorithm - which results in a significant performance boost in terms of clustering quality.