Aggregate a frame into clusters. Each cluster consists of a set of members, i.e. a subset of the input frame, and is represented by an exemplar, i.e. one of the members.

For one- and two-column frames the aggregation is based on the standard equal-interval binning for numeric columns and grouping operation for string columns.

In the general case, a parallel one-pass ad hoc algorithm is employed. It starts with an empty exemplar list and does one pass through the data. If a partucular observation falls into a bubble with a given radius and the center being one of the exemplars, it marks this observation as a member of that exemplar’s cluster. If there is no appropriate exemplar found, the observation is marked as a new exemplar.

If the fixed_radius is None, the algorithm will start with the delta, that is radius squared, being equal to the machine precision. When the number of gathered exemplars becomes larger than nd_max_bins, the following procedure is performed:

  • find the mean distance between all the gathered exemplars;

  • merge all the exemplars that are within the half of this distance;

  • adjust delta by taking into account the initial bubble radius;

  • save the exemplar’s merging information for the final processing.

If the fixed_radius is set to a valid numeric value, the algorithm will stick to that value and will not adjust delta.

Note: the general n-dimensional algorithm takes into account the numeric columns only, and all the other columns are ignored.



The input frame containing numeric or string columns.


Minimum number of rows the input frame should have to be aggregated. If frame has less rows than min_rows, aggregation is bypassed, in the sence that all the input rows become exemplars.


Number of bins for 1D aggregation.


Number of bins for the first column for 2D aggregation.


Number of bins for the second column for 2D aggregation.


Maximum number of exemplars for ND aggregation. It is guaranteed that the ND algorithm will return less than nd_max_bins exemplars, but the exact number may vary from run to run due to parallelization.


Number of columns at which the projection method is used for ND aggregation.


Seed to be used for the projection method.


An option to indicate whether double precision, i.e. float64, or single precision, i.e. float32, arithmetic should be used for computations.


Fixed radius for ND aggregation, use it with caution. If set, nd_max_bins will have no effect and in the worst case number of exemplars may be equal to the number of rows in the data. For big data this may result in extremly large execution times. Since all the columns are normalized to [0, 1), the fixed_radius value should be chosen accordingly.

Tuple[Frame, Frame]

The first element in the tuple is the aggregated frame, i.e. the frame containing exemplars, with the shape of (nexemplars, frame.ncols + 1), where nexemplars is the number of gathered exemplars. The first frame.ncols columns are the columns from the input frame, and the last column is the members_count that has stype int32 containing number of members per exemplar.

The second element in the tuple is the members frame with the shape of (frame.nrows, 1). Each row in this frame corresponds to the row with the same id in the input frame. The single column exemplar_id has an stype of int32 and contains the exemplar ids that a particular member belongs to. These ids are effectively the ids of the exemplar’s rows from the input frame.


The exception is raised when one of the frame’s columns has an unsupported stype, i.e. there is a column that is both non-numeric and non-string.