Comments (1)
If a data.frame is specified, then Euclidean distance is applied. You have to convert the data to all numeric values (e.g., via dummy encoding). Alternatively, you can precalculate a distance matrix using the appropriate distance measure and supply that to dbscan.
from dbscan.
Related Issues (20)
- Implement Density-Based Clustering Validation (DBCV) HOT 4
- BD-trees
- hdbscan HOT 2
- LOF edge case HOT 2
- LOF fails after upgrading to dbscan 1.1-6 HOT 2
- Possible Memory Leak HOT 2
- kNN crashing (segfault) when matrix has Inf values HOT 1
- mrdist error in large datasets HOT 3
- frNN object created from scratch couldn't be used in dbscan HOT 6
- Error in mrd(x_dist, coredist) : number of mutual reachability distance values and size of the distances do not agree. HOT 6
- DBSCAN for trajectories HOT 4
- Getting an error when using predict: x has to be a numeric matrix. HOT 2
- may you clarify is multi-density clustering is implemented, since it is mentioned on references ? HOT 1
- R session aborted in pointdensity() HOT 4
- Add broom tidier methods HOT 9
- Allow setting cluster_selection_epsilon in hdbscan() HOT 6
- `hdbscan` documenting params that are not used HOT 1
- Add minimum R version to DESCRIPTION
- pkgdown website for package HOT 2
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from dbscan.