Quantum clustering
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Quantum Clustering (QC), is a data clustering algorithm accomplished by substituting each point in a given dataset with a Gaussian. The width of the Gaussian is a sigma value, a hyper-parameter which can be manually defined and manipulated to suit the application. Gradient descent is then used to "move" the points to their local minima. These local minima then define the cluster centers. QC has not been evaluated against traditional modern clustering algorithms, aside from Jaccard scoring. QC has thus far failed to produce separations with enough variance to exploit at big data scale.
References
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- Yao, Z.; Peng, W.; Gao-yun, C.; Dong-Dong, C.; Rui, Ding; Yan, Z (2008) Quantum Clustering Algorithm based on Exponent Measuring Distance