DBSCAN - Density-Based Spatial Clustering of Applications with Noise. Finds core samples of high density and expands clusters from them. Good for data which contains clusters of similar density. This is a wrapper around the Python class sklearn.cluster.DBSCAN.

## References

Ester, M., H. P. Kriegel, J. Sander, and X. Xu (1996).

*A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise*, In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland, OR, AAAI Press, pp. 226-231.Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017).

*DBSCAN revisited, revisited: why and how you should (still) use DBSCAN*, ACM Transactions on Database Systems (TODS),**42**(3), p. 19.

## Super classes

`rgudhi::PythonClass`

-> `rgudhi::SKLearnClass`

-> `rgudhi::BaseClustering`

-> `DBSCAN`

## Methods

## Inherited methods

### Method `new()`

The DBSCAN class constructor.

#### Arguments

`eps`

A numeric value specifying the maximum distance between two samples for one to be considered as in the neighborhood of the other. This is not a maximum bound on the distances of points within a cluster. This is the most important DBSCAN parameter to choose appropriately for your data set and distance function. Defaults to

`0.5`

.`min_samples`

An integer value specifying the number of samples (or total weight) in a neighborhood for a point to be considered as a core point. This includes the point itself. Defaults to

`5L`

.`metric`

Either a string or an object coercible into a function via

`rlang::as_function()`

specifying the metric to use when calculating distance between instances in a feature array. If`metric`

is a string, it must be one of the options allowed by sklearn.metrics.pairwise_distances for its`metric`

parameter. If`metric`

is`"precomputed"`

,`X`

is assumed to be a distance matrix and must be square.`X`

may be a sparse graph, in which case only*nonzero*elements may be considered neighbors for DBSCAN. Defaults to`"euclidean"`

.`metric_params`

A named list specifying additional parameters to be passed on to the metric function. Defaults to

`NULL`

.`algorithm`

A string specifying the algorithm to be used by the sklearn.neighbors.NearestNeighbors module to compute pointwise distances and find nearest neighbors. Choices are

`"auto"`

,`"ball_tree"`

,`"kd_tree"`

or`"brute"`

. Defaults to`"auto"`

.`leaf_size`

An integer value specifying the leaf size passed to sklearn.neighbors.BallTree or sklearn.neighbors.KDTree. This can affect the speed of the construction and query, as well as the memory required to store the tree. The optimal value depends on the nature of the problem. Defaults to

`30L`

.`p`

An integer value specifying the power of the Minkowski metric to be used to calculate distance between points. Defaults to

`2L`

.`n_jobs`

An integer value specifying the number of parallel jobs to run. Defaults to

`1L`

.