DCPY.DBSCANCLUST(min_samples, eps, columns)
The DBSCAN (Density Based Spatial Clustering of Applications with Noise) clustering algorithm views clusters as highdensity areas separated by low density areas, when finding core samples and expanding clusters from them. Euclidean distance is used as a measure when calculating distance between data points.
Parameters

min_samples – The number of data points in a neighborhood for a point to be considered as a core point, integer (default 5).

eps – Maximum distance between two samples for them to be considered as the same neighborhood, float (default 0.5).

columns – Dataset columns or custom calculations.
Example: DCPY.DBSCANCLUST(5, 0.5, sum([Gross Sales]), sum([No of customers])) used as a calculation for the Color field of the Scatterplot visualization.
Input data
 Numeric variables are automatically scaled to zero mean and unit variance.
 Character variables are transformed to numeric values using onehot encoding.
 Dates are treated as character variables, so they are also onehot encoded.
 Size of input data is not limited, but many categories in character or date variables increase rapidly the dimensionality.
 Rows that contain missing values in any of their columns are dropped.
Result
 Column of integer values starting with 1, where each number corresponds to a cluster assigned to each record (row) by the algorithm. Data points that do not belong to any cluster are considered as noise (or outliers) and are assigned to 1.
 Rows that were dropped from input data due to containing missing value have missing value instead of assigned inlier/outlier value.
Key usage points
 Automatically estimates optimum number of clusters, which can be controlled with min_samples and eps parameters.
 Not all data points are assigned to a cluster. Data points that do not belong to any cluster are considered as noise (or outliers).
 Clusters can be of any shape, but should be of similar density.
 Can find clusters completely surrounded by different clusters.
 Robust towards outliers (noise).
 Sensitivity to order of the data.
 Does not work well if clusters vary in their density.
 Not scalable with number of records and memory usage inefficiency.
 Results are very sensitive to min_samples and eps parameters.
 Suffers from 'curse of dimensionality', which may result in misleading result when the number of variables is high.
For the whole list of algorithms, see Data science builtin algorithms.