This endpoint runs the kmeans algorithm  a heuristic algorithm that attempts to do kmeans clustering. An ideal kmeans 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 kmeans 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 (GPUbased) build to service this request.
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 ( {} ).

Name  Type  Description 

means  array of arrays of doubles  The kmean values found. 
counts  array of longs  The number of elements in the cluster closest the corresponding kmeans values. 
rms_dists  array of doubles  The root mean squared distance of the elements in the cluster for each of the kmeans 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 kmeans 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. 
info  map of string to strings  Additional information. 