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, in [schema_name.]table_name format, using standard name resolution rules. | ||||||||||||||||
| 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 ( {} ).
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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. | ||||
| info | map of string to strings | Additional information. The default value is an empty map ( {} ).
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