DBSCAN Revisited, Revisited
Heidelberg University · University of Alberta · +3 more institutions
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…
Citation impact
- FWCI
- 69.45
- Percentile
- 100%
- References
- 49
Authors
5Topics & keywords
- Computer science
- DBSCAN
- Data mining
- Heuristics
- Information retrieval
- Algorithm
- Artificial intelligence
- Cluster analysis