Explainable Clustering of Mixture Models
This paper proposes an algorithm that leverages information about the distribution of the data to find better cuts, and extends the algorithm and the theoretical guarantees it provides to kernel clustering, thereby refining the existing worst-case analysis.
Maximilian Fleissner, Maedeh Zarvandi, D. Ghoshdastidar
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