mlpack::dbscan::DBSCAN< RangeSearchType, PointSelectionPolicy > Class Template Reference

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a clustering technique described in the following paper: More...

#include <dbscan.hpp>

Public Member Functions
	DBSCAN (const double epsilon, const size_t minPoints, const bool batchMode=true, RangeSearchType rangeSearch=RangeSearchType(), PointSelectionPolicy pointSelector=PointSelectionPolicy())
	Construct the DBSCAN object with the given parameters. More...
template<typename MatType >
size_t	Cluster (const MatType &data, arma::mat &centroids)
	Performs DBSCAN clustering on the data, returning number of clusters and also the centroid of each cluster. More...
template<typename MatType >
size_t	Cluster (const MatType &data, arma::Row< size_t > &assignments)
	Performs DBSCAN clustering on the data, returning number of clusters and also the list of cluster assignments. More...
template<typename MatType >
size_t	Cluster (const MatType &data, arma::Row< size_t > &assignments, arma::mat &centroids)
	Performs DBSCAN clustering on the data, returning number of clusters, the centroid of each cluster and also the list of cluster assignments. More...

Detailed Description

template<typename RangeSearchType = range::RangeSearch<>, typename PointSelectionPolicy = OrderedPointSelection>
class mlpack::dbscan::DBSCAN< RangeSearchType, PointSelectionPolicy >

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a clustering technique described in the following paper:

@inproceedings{ester1996density,
  title={A density-based algorithm for discovering clusters in large spatial
      databases with noise.},
  author={Ester, M. and Kriegel, H.-P. and Sander, J. and Xu, X.},
  booktitle={Proceedings of the Second International Conference on Knowledge
      Discovery and Data Mining (KDD '96)},
  pages={226--231},
  year={1996}
}

The DBSCAN algorithm iteratively clusters points using range searches with a specified radius parameter. This implementation allows configuration of the range search technique used and the point selection strategy by means of template parameters.

Template Parameters

RangeSearchType	Class to use for range searching.
PointSelectionPolicy	Strategy for selecting next point to cluster with.

Constructor & Destructor Documentation

DBSCAN()

template<typename RangeSearchType , typename PointSelectionPolicy >

mlpack::dbscan::DBSCAN< RangeSearchType, PointSelectionPolicy >::DBSCAN	(	const double	epsilon,
		const size_t	minPoints,
		const bool	batchMode = true,
		RangeSearchType	rangeSearch = RangeSearchType(),
		PointSelectionPolicy	pointSelector = PointSelectionPolicy()
	)

Construct the DBSCAN object with the given parameters.

The batchMode parameter should be set to false in the case where RAM issues will be encountered (i.e. if the dataset is very large or if epsilon is large). When batchMode is false, each point will be searched iteratively, which could be slower but will use less memory.

Parameters

epsilon	Size of range query.
minPoints	Minimum number of points for each cluster.
batchMode	If true, all points are searched in batch.
rangeSearch	Optional instantiated RangeSearch object.
pointSelector	OptionL instantiated PointSelectionPolicy object.

Member Function Documentation

Cluster() [1/3]

template<typename RangeSearchType , typename PointSelectionPolicy >

template<typename MatType >

size_t mlpack::dbscan::DBSCAN< RangeSearchType, PointSelectionPolicy >::Cluster	(	const MatType &	data,
		arma::mat &	centroids
	)

Performs DBSCAN clustering on the data, returning number of clusters and also the centroid of each cluster.

Template Parameters

MatType

Type of matrix (arma::mat or arma::sp_mat).

Parameters

data	Dataset to cluster.
centroids	Matrix in which centroids are stored.

Cluster() [2/3]

template<typename RangeSearchType , typename PointSelectionPolicy >

template<typename MatType >

size_t mlpack::dbscan::DBSCAN< RangeSearchType, PointSelectionPolicy >::Cluster	(	const MatType &	data,
		arma::Row< size_t > &	assignments
	)

Performs DBSCAN clustering on the data, returning number of clusters and also the list of cluster assignments.

Performs DBSCAN clustering on the data, returning the number of clusters and also the list of cluster assignments.

If assignments[i] == SIZE_MAX, then the point is considered "noise".

Template Parameters

MatType

Type of matrix (arma::mat or arma::sp_mat).

Parameters

data	Dataset to cluster.
assignments	Vector to store cluster assignments.

Cluster() [3/3]

template<typename RangeSearchType , typename PointSelectionPolicy >

template<typename MatType >

size_t mlpack::dbscan::DBSCAN< RangeSearchType, PointSelectionPolicy >::Cluster	(	const MatType &	data,
		arma::Row< size_t > &	assignments,
		arma::mat &	centroids
	)

Performs DBSCAN clustering on the data, returning number of clusters, the centroid of each cluster and also the list of cluster assignments.

If assignments[i] == SIZE_MAX, then the point is considered "noise".

Template Parameters

MatType

Type of matrix (arma::mat or arma::sp_mat).

Parameters

data	Dataset to cluster.
assignments	Vector to store cluster assignments.
centroids	Matrix in which centroids are stored.

---

The documentation for this class was generated from the following files:

• src/mlpack/methods/dbscan/dbscan.hpp
• src/mlpack/methods/dbscan/dbscan_impl.hpp