<ul data-eligibleForWebStory="true"><li>Density-based clustering methods are more effective than centroid-based ones for data with noise or diverse distributions.</li><li>A recent study introduces a key property related to the number of clusters and core point radius in density-based clustering.</li><li>New strategies for determining radius values more efficiently using the Ternary Search algorithm are proposed based on this property.</li><li>Extensive validation on various high-dimensional data tasks shows the practical effectiveness and robustness of the proposed methodology.</li></ul>

Unimodal Strategies in Density-Based Clustering

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