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A Probabilistic framework for Quantum Clustering
[article]
2019
arXiv
pre-print
Quantum Clustering is a powerful method to detect clusters in data with mixed density. However, it is very sensitive to a length parameter that is inherent to the Schr\"odinger equation. In addition, linking data points into clusters requires local estimates of covariance that are also controlled by length parameters. This raises the question of how to adjust the control parameters of the Schr\"odinger equation for optimal clustering. We propose a probabilistic framework that provides an
arXiv:1902.05578v1
fatcat:365ltndfpja2zpirugfyc5jupe