DMDHC: Discovery of Multi-Density Hierarchical Cluster Structures

Walid Durani, Dominik Mautz, Claudia Plant, Christian Böhm

Abstract: Hierarchical clustering techniques can reveal nested structures within data by representing patterns in a tree-like form. However, when dealing with complex data, many traditional hierarchical methods produce cluttered and hard-to-interpret trees. To address this, we propose a novel hierarchical clustering method called Discovery of Multi-Density Hierarchical Cluster structures (DMDHC), which introduces a new type of cluster tree to represent hierarchical information more effectively. Our approach automatically generates hierarchical local cuts along the tree structure. In contrast to state-of-the-art methods like PEARCH, which typically apply only a single cut across the hierarchy, DMDHC takes advantage of density-based insights to perform multiple cuts at different levels. This results in a more compact and comprehensible representation of intricate hierarchical structures. Extensive experiments on real- world datasets demonstrate that DMDHC, along with its newly introduced tree structure, outperforms existing methods.