DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation

@article{Gan2015DBSCANRM,
  title={DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation},
  author={Junhao Gan and Yufei Tao},
  journal={Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data},
  year={2015}
}
  • Junhao Gan, Yufei Tao
  • Published 27 May 2015
  • Computer Science
  • Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data
DBSCAN is a popular method for clustering multi-dimensional objects. [] Key Result We formalize our findings into the new notion of ρ-<i>approximate</i> DBSCAN, which we believe should replace DBSCAN on big data due to the latter's computational intractability.
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165 Citations
Highly Influential Citations
24
Background Citations
67
Methods Citations
40
Results Citations
7

165 Citations

Citation Type

Citation Type

On the Hardness and Approximation of Euclidean DBSCAN
TLDR
It is proved that, for d ≥3, the problem of computing DBSCAN clusters from scratch requires ω(n 4/3) time to solve, unless very significant breakthroughs—ones widely believed to be impossible—could be made in theoretical computer science.
DBSCAN Revisited, Revisited
TLDR
In new experiments, it is shown that the new SIGMOD 2015 methods do not appear to offer practical benefits if the DBSCAN parameters are well chosen and thus they are primarily of theoretical interest.
Grid-based DBSCAN: Indexing and inference
A fast clustering algorithm based on pruning unnecessary distance computations in DBSCAN for high-dimensional data
On Metric DBSCAN with Low Doubling Dimension
TLDR
This paper considers the metric DBSCAN problem under the assumption that the inliers (excluding the outliers) have a low doubling dimension and applies a novel randomized $k$-center clustering idea to reduce the complexity of range query, which is the most time consuming step in the whole DBS CAN procedure.
BLOCK-DBSCAN: Fast clustering for large scale data
KNN-BLOCK DBSCAN: Fast Clustering for Large-Scale Data
TLDR
A simple but fast approximate DBSCAN is proposed based on two findings: 1) the problem of identifying whether a point is a core point or not is, in fact, a kNN problem and 2) a point has a similar density distribution to its neighbors, and neighbor points are highly possible to be the same type (core point, border point, or noise).
DBSCAN++: Towards fast and scalable density clustering
TLDR
Surprisingly, up to a certain point, one 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.
Dynamic Density Based Clustering
TLDR
It is proved that the ρ-approximate version of DBSCAN suffers from the very same hardness when the dataset is fully dynamic, namely, when both insertions and deletions are allowed, and it is shown that this issue goes away as soon as tiny further relaxation is applied, yet still ensuring the same quality---known as the ``sandwich guarantee''---of ρ.
An Efficient Density-based Clustering Algorithm for Higher-Dimensional Data
TLDR
A novel algorithm named GDPAM is proposed attempting to extend Grid-based DBSCAN to higher data dimension by adopting an efficient union-find algorithm to maintain the clustering information in order to reduce redundancies in the merging.
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