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Found 4 papers, 2 papers with code
Deep Clustering Using the Soft Silhouette Score: Towards Compact and Well-Separated Clusters
Soft silhouette rewards compact and distinctly separated clustering solutions like the conventional silhouette coefficient.
Silhouette Aggregation: From Micro to Macro
Silhouette coefficient is an established internal clustering evaluation measure that produces a score per data point, assessing the quality of its clustering assignment.
UniForCE: The Unimodality Forest Method for Clustering and Estimation of the Number of Clusters
In this work, we focus on the concept of unimodality and propose a flexible cluster definition called locally unimodal cluster.
Global $k$-means$++$: an effective relaxation of the global $k$-means clustering algorithm
The global $k$-means is a deterministic algorithm proposed to tackle the random initialization problem of k-means but its well-known that requires high computational cost.
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