HDBSCAN*, a state-of-the-art density-based hierarchical clustering method, produces organization of clusters in dataset w.r.t. parameter mpts. While small change mpts typically leads to the structure, choosing “good” value can be challenging: depending on data distribution, high or low may more appropriate, and certain reveal themselves at different values. To explore results for range values, ...

Abstract Clustering algorithms are of fundamental importance when dealing with large unstructured datasets and discovering new patterns correlations therein, applications ranging from scientific research to medical imaging marketing analysis. In this work, we introduce a quantum version the density peak clustering algorithm, built upon routine for minimum finding. We prove speedup decision depe...

Ant-based clustering clustering methods have received significant attention as robust methods for clustering. Most ant-based algorithms use local density as a metric for determining the ants’ propensities to pick up or deposit a data item; however a number of authors in classical clustering methods have pointed out the advantages of entropy-based metrics for clustering. We introduce an entropy ...