swMATH ID: 34904
Software Authors: Jennifer Jang, Heinrich Jiang
Description: DBSCAN++: Towards fast and scalable density clustering. DBSCAN is a classical density-based clustering procedure with tremendous practical relevance. However, DBSCAN implicitly needs to compute the empirical density for each sample point, leading to a quadratic worst-case time complexity, which is too slow on large datasets. We propose DBSCAN++, a simple modification of DBSCAN which only requires computing the densities for a chosen subset of points. We show empirically that, compared to traditional DBSCAN, DBSCAN++ can provide not only competitive performance but also added robustness in the bandwidth hyperparameter while taking a fraction of the runtime. We also present statistical consistency guarantees showing the trade-off between computational cost and estimation rates. 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.
Homepage: https://arxiv.org/abs/1810.13105
Source Code: https://github.com/jenniferjang/dbscanpp
Keywords: Machine Learning (cs.LG); Machine Learning (stat.ML)
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Cited in: 1 Publication

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