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Found 4 papers, 1 papers with code
MeanShift++: Extremely Fast Mode-Seeking With Applications to Segmentation and Object Tracking
The runtime is linear in the number of points and exponential in dimension, which makes MeanShift++ ideal on low-dimensional applications such as image segmentation and object tracking.
Faster DBSCAN via subsampled similarity queries
DBSCAN is a popular density-based clustering algorithm.
DBSCAN++: Towards fast and scalable density clustering
Surprisingly, up to a certain point, we can enjoy the same estimation rates while lowering computational cost, showing that DBSCAN++ is a sub-quadratic algorithm that attains minimax optimal rates for level-set estimation, a quality that may be of independent interest.
Quickshift++: Provably Good Initializations for Sample-Based Mean Shift
We provide initial seedings to the Quick Shift clustering algorithm, which approximate the locally high-density regions of the data.
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