Generalized density clustering
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In this article, generalized density-based clustering in which sharply defined clusters such as clusters on lower-dimensional manifolds are allowed was studied and it was shown that accurate clustering is possible even in high dimensions.Abstract:
We study generalized density-based clustering in which sharply defined clusters such as clusters on lower-dimensional manifolds are allowed. We show that accurate clustering is possible even in high dimensions. We propose two data-based methods for choosing the bandwidth and we study the stability properties of density clusters. We show that a simple graph-based algorithm successfully approximates the high density clusters.read more
Citations
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Journal ArticleDOI
Discussion on "Stability Selection" by Meinshausen and Buhlmann
顏佑銘,Tso-Jung Yen,Yu-Min Yen +2 more
Proceedings ArticleDOI
Accelerated Hierarchical Density Based Clustering
Leland McInnes,John Healy +1 more
TL;DR: In this paper, the authors presented an accelerated algorithm for hierarchical density based clustering, which provides comparable performance to DBSCAN, while supporting variable density clusters, and eliminating the need for the difficult to tune distance scale parameter epsilon.
Journal ArticleDOI
A Tutorial on Kernel Density Estimation and Recent Advances
TL;DR: In this article, a tutorial provides a gentle introduction to kernel density estimation (KDE) and recent advances regarding confidence bands and geometric/topological features, and a discussion of basi...
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A Tutorial on Kernel Density Estimation and Recent Advances
TL;DR: This tutorial provides a gentle introduction to kernel density estimation (KDE) and recent advances regarding confidence bands and geometric/topological features, and illustrates how one can use KDE to estimate a cumulative distribution function and a receiver operating characteristic curve.
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Distribution-Free Prediction Sets
TL;DR: This article considers the problem of constructing nonparametric tolerance/prediction sets by starting from the general conformal prediction approach, and uses a kernel density estimator as a measure of agreement between a sample point and the underlying distribution.
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