The floating-point type that the algorithm uses for intermediate computations. Can be
Available methods for computation of DBSCAN algorithm:
The maximum distance between observations lying in the same neighborhood.
The total weights of observations in a neighborhood for an observation to be considered as a core one.
If flag is set to false, all neighborhoods will be computed and stored prior to clustering. It will require up to O(|sum of sizes of neighborhoods of all observation|) additional memory, which in worst case can be O(|number of observations|^2). However, in common case performance may be better.
The 64-bit integer flag that specifies which extra characteristics of the DBSCAN to compute.
Provide one of the following values to request a single characteristic or use bitwise OR to request a combination of the characteristics:
Pointer to the
pnumeric table with the data to be clustered. The input can be an object of any class derived from
Optional input. Pointer to the
nx 1 numeric table with weights of observations.
The input can be an object of any class derived from
PackedSymmetricMatrix. By default all weights are equal to 1.
Pointer to the
nx 1 numeric table with assignments of cluster indices to observations in the input data. Noise observations have the assignment equal to -1.
Pointer to the 1 x 1 numeric table with the total number of clusters found by the algorithm.
Pointer to the numeric table with 1 column and arbitrary number of rows, containing indices of core observations.
Pointer to the numeric table with
pcolumns and arbitrary number of rows, containing core observations