cuda_ml_dbscan {cuda.ml} | R Documentation |

## Run the DBSCAN clustering algorithm.

### Description

Run the DBSCAN (Density-based spatial clustering of applications with noise) clustering algorithm.

### Usage

```
cuda_ml_dbscan(
x,
min_pts,
eps,
cuML_log_level = c("off", "critical", "error", "warn", "info", "debug", "trace")
)
```

### Arguments

`x` |
The input matrix or dataframe. Each data point should be a row and should consist of numeric values only. |

`min_pts` , `eps` |
A point 'p' is a core point if at least 'min_pts' are within distance 'eps' from it. |

`cuML_log_level` |
Log level within cuML library functions. Must be one of "off", "critical", "error", "warn", "info", "debug", "trace". Default: off. |

### Value

A list containing the cluster assignments of all data points. A data point not belonging to any cluster (i.e., "noise") will have NA its cluster assignment.

### Examples

```
library(cuda.ml)
library(magrittr)
gen_pts <- function() {
centroids <- list(c(1000, 1000), c(-1000, -1000), c(-1000, 1000))
pts <- centroids %>%
purrr::map(~ MASS::mvrnorm(10, mu = .x, Sigma = diag(2)))
rlang::exec(rbind, !!!pts)
}
m <- gen_pts()
clusters <- cuda_ml_dbscan(m, min_pts = 5, eps = 3)
print(clusters)
```

[Package

*cuda.ml*version 0.3.2 Index]