aggregate_k_means
- aggregate_k_means(
table_name: string,
column_names: string[],
k: number,
tolerance: number,
options?: Record<string, string>,
callback?: GPUdbCallback,
): Promise<AggregateKMeansResponse>Parameters
- 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: string[]
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: number
The number of mean points to be determined by the algorithm.
- tolerance: number
Stop iterating when the distances between successive points is less than the given tolerance.
Optionaloptions: Record<string, string>Optional parameters.
Optionalcallback: GPUdbCallbackOptional callback function
Returns Promise<AggregateKMeansResponse>
Promise<Types.AggregateKMeansResponse> - Promise resolving to the response
- Defined in src/GPUdb.ts:3171
- table_name: string
aggregate_k_means_request
- aggregate_k_means_request(
request: AggregateKMeansRequest,
callback?: GPUdbCallback,
): Promise<AggregateKMeansResponse>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.
Parameters
- request: AggregateKMeansRequest
Request object containing the parameters for the operation
Optionalcallback: GPUdbCallbackOptional callback function
Returns Promise<AggregateKMeansResponse>
Promise<Types.AggregateKMeansResponse> - Promise resolving to the response
- Defined in src/GPUdb.ts:3131
- request: AggregateKMeansRequest
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.