DBSCAN
Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm proposed in [Ester96].
It is a density-based clustering non-parametric algorithm: given a set of observations in some space,
it groups together observations that are closely packed together (observations with many nearby neighbors),
marking as outliers observations that lie alone in low-density regions (whose nearest neighbors are too far away).
Operation | Computational methods | Programming Interface |
Mathematical formulation
Computation
Given the set 
of 
-dimensional feature vectors (further referred as observations),
a positive floating-point number 
and 
are considered to be in the same cluster if there is a core observation 
,
and and are both reachable from .
of -dimensional feature vectors (further referred as observations),
a positive floating-point number epsilon
and a positive integer minObservations
,
the problem is to get clustering assignments for each input observation, based on the definitions below [Ester96]:
two observations and are considered to be in the same cluster if there is a core observation ,
and and are both reachable from .Each cluster gets a unique identifier, an integer number from 
to 
.
Each observation is assigned an identifier of the cluster it belongs to,
or 
if the observation considered to be a noise observation.
to .
Each observation is assigned an identifier of the cluster it belongs to,
or if the observation considered to be a noise observation.Programming Interface
Refer to API Reference: DBSCAN.
Distributed mode
The algorithm supports distributed execution in SMPD mode (only on GPU).
Usage example
Compute
void run_compute(const table& data,
const table& weights) {
double epsilon = 1.0;
std::int64_t max_observations = 5;
const auto dbscan_desc = kmeans::descriptor<float>{epsilon, max_observations}
.set_result_options(dal::dbscan::result_options::responses);
const auto result = compute(dbscan_desc, data, weights);
print_table("responses", result.get_responses());
}Examples
oneAPI DPC++
Batch Processing:
oneAPI C++
Batch Processing:
Python* with DPC++ support
Batch Processing: