Hello, I'm a researcher at Google Research and I am writing to initiate discussion about adding Quickshift as well as a variant of it as part of scikit-learn's set of clustering algorithms.
This somewhat recent algorithm was designed as a faster alternative to Mean Shift and has been used extensively in computer vision (and already part of scikit-image). The method was published independently in these papers [1,2]. [1] has 600 citations and [2] has 1300 citations. [1] Vedaldi, Andrea, and Stefano Soatto. "Quick shift and kernel methods for mode seeking." *European Conference on Computer Vision*. Springer, Berlin, Heidelberg, 2008. [2] Rodriguez, Alex, and Alessandro Laio. "Clustering by fast search and find of density peaks." *Science* 344.6191 (2014): 1492-1496. In addition to Quickshift, I also propose a variant called Quickshift++, which is Quickshift with an additional hyperparameter. We showed in [3] that this substantially improved performance over Quickshift as well as other clustering algorithms implemented in sklearn on benchmark datasets. (i.e. Figure 9 in https://arxiv.org/abs/1805.07909) and was published at ICML 2018. [3] Jiang, Heinrich, Jennifer Jang, and Samory Kpotufe. "Quickshift++: Provably Good Initializations for Sample-Based Mean Shift." ICML 2018 We have an implementation here (https://github.com/google/quickshift). Best, Heinrich
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