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HDBSCAN*, a state-of-the-art density-based hierarchical clustering method,produces a hierarchical organization of clusters in a dataset w.r.t. aparameter mpts. While the performance of HDBSCAN* is robust w.r.t. mpts in thesense that a small change in mpts typically leads to only a small or no changein the clustering structure, choosing a "good" mpts value can be challenging:depending on the data distribution, a high or low value for mpts may be moreappropriate, and certain data clusters may reveal themselves at differentvalues of mpts. To explore results for a range of mpts values, however, one hasto run HDBSCAN* for each value in the range independently, which iscomputationally inefficient. In this paper, we propose an efficient approach tocompute all HDBSCAN* hierarchies for a range of mpts values by replacing thegraph used by HDBSCAN* with a much smaller graph that is guaranteed to containthe required information. An extensive experimental evaluation shows that withour approach one can obtain over one hundred hierarchies for the computationalcost equivalent to running HDBSCAN* about 2 times.

Efficient Computation of Multiple Density-Based Clustering Hierarchies