Scalable clustering by aggregating representatives in hierarchical groups

Appropriately handling the scalability of clustering is a long-standing challenge for the study of clustering techniques and is of fundamental interest to researchers in the community of data mining and knowl-edge discovery. In comparison to other clustering methods, hierarchical clustering demonstrates better interpretability of clustering results but poor scalability while handling large-scale data. Thus, more com-prehensive studies on this problem need to be conducted. This paper develops a new scalable hierarchical clustering model called Election Tree, which can detect the most representative point for each sub-cluster via the process of node election in split data and adjust the members in sub-clusters by the operations of node merging and swap. Extensive experiments on real-world datasets reveal that the proposed compu-tational framework has better clustering accuracy as opposed to the competing baseline methods. Mean-while, with respect to the scalability tests on incremental synthetic datasets, the results show that the new model has a significantly lower time consumption than the state-of-the-art hierarchical clustering models such as PERCH, GRINCH, SCC and other classic baselines.(c) 2022 Elsevier Ltd. All rights reserved.