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A cutting plane algorithm for globally solving low dimensional k-means clustering problems
Clustering is one of the most fundamental tools in data science and machine learning, and k-means clustering is one of the most common such methods.
Orthogonalization of data via Gromov-Wasserstein type feedback for clustering and visualization
In this paper we propose an adaptive approach for clustering and visualization of data by an orthogonalization process.
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