ACM Transactions on Database Systems· 2017Q2
DBSCAN Revisited, Revisited
- 2,652citations
- Q2SCImago
- 2017year
Short summary
This paper argues that a previous critique of the DBSCAN algorithm was misdirected, attributing performance issues to spatial index assumptions rather than the algorithm itself.
AI-generated from the title and abstract; the full text is not read.
Key points
- The 2015 'DBSCAN Revisited' paper's critique was misdirected at the DBSCAN algorithm itself.
- Performance issues were likely due to assumptions about spatial index structures (e.g., R-trees), not DBSCAN.
- Original DBSCAN with effective indexing and reasonable parameters performs competitively.
- Newer methods from the 2015 paper offer minimal practical benefits when DBSCAN parameters are well-chosen.
AI-generated from the title and abstract; the full text is not read.
Abstract
At SIGMOD 2015, an article was presented with the title “DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation” that won the conference’s best paper award. In this technical correspondence, we want to point out some inaccuracies in the way DBSCAN was represented, and why the criticism should have been directed at the assumption about the performance of spatial index structures such as R-trees and not at an algorithm that can use such indexes. We will also discuss the relationship of DBSCAN performance and the indexability of the dataset, and discuss some heuristics for choosing appropriate DBSCAN parameters. Some indicators of bad parameters will be proposed to help guide future users of this algorithm in choosing parameters such as to obtain both meaningful results and good performance. In new experiments, we show 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. In conclusion, the original DBSCAN algorithm with effective indexes and reasonably chosen parameter values performs competitively compared to the method proposed by Gan and Tao.
The authors' abstract, as published at the source. ACM Transactions on Database Systems, 2017 · DOI ↗
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Field: Signal Processing
Signal ProcessingComputer Science