Aggregate K-means

This endpoint runs the k-means algorithm - a heuristic algorithm that attempts to do k-means clustering. An ideal k-means clustering algorithm selects k points such that the sum of the mean squared distances of each member of the set to the nearest of the k points is minimized. The k-means algorithm however does not necessarily produce such an ideal cluster. It begins with a randomly selected set of k points and then refines the location of the points iteratively and settles to a local minimum. Various parameters and options are provided to control the heuristic search.

NOTE: The Kinetica instance being accessed must be running a CUDA (GPU-based) build to service this request.

Input Parameter Description

Name	Type	Description
table_name	string	Name of the table on which the operation will be performed. Must be an existing table or collection.
column_names	array of strings	List of column names on which the operation would be performed. If n columns are provided then each of the k result points will have n dimensions corresponding to the n columns.
k	int	The number of mean points to be determined by the algorithm.
tolerance	double	Stop iterating when the distances between successive points is less than the given tolerance.
options	map of string to strings	Optional parameters. The default value is an empty map ( {} ). Supported Parameters (keys) Parameter Description whiten When set to 1 each of the columns is first normalized by its stdv - default is not to whiten. max_iters Number of times to try to hit the tolerance limit before giving up - default is 10. num_tries Number of times to run the k-means algorithm with a different randomly selected starting points - helps avoid local minimum. Default is 1.

Output Parameter Description

Name	Type	Description
means	array of arrays of doubles	The k-mean values found.
counts	array of longs	The number of elements in the cluster closest the corresponding k-means values.
rms_dists	array of doubles	The root mean squared distance of the elements in the cluster for each of the k-means values.
count	long	The total count of all the clusters - will be the size of the input table.
rms_dist	double	The sum of all the rms_dists - the value the k-means algorithm is attempting to minimize.
tolerance	double	The distance between the last two iterations of the algorithm before it quit.
num_iters	int	The number of iterations the algorithm executed before it quit.