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    aggregateKMeans

    public AggregateKMeansResponse aggregateKMeans(AggregateKMeansRequest request) throws GPUdbException
    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 - Request object containing the parameters for the operation.
    Returns:
    Response object containing the results of the operation.
    Throws:
    GPUdbException - if an error occurs during the operation.

  • ​
    aggregateKMeans

    public AggregateKMeansResponse aggregateKMeans(String tableName, List<String> columnNames, int k, double tolerance, Map<String,String> options) throws GPUdbException
    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:
    tableName - 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.
    columnNames - 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 - The number of mean points to be determined by the algorithm.
    tolerance - Stop iterating when the distances between successive points is less than the given tolerance.
    options - Optional parameters.
    • 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.
    • CREATE_TEMP_TABLE: If TRUE, a unique temporary table name will be generated in the sys_temp schema and used in place of RESULT_TABLE. If RESULT_TABLE_PERSIST is FALSE (or unspecified), then this is always allowed even if the caller does not have permission to create tables. The generated name is returned in QUALIFIED_RESULT_TABLE_NAME. Supported values:The default value is FALSE.
    • RESULT_TABLE: The name of a table used to store the results, in [schema_name.]table_name format, using standard name resolution rules and meeting table naming criteria. If this option is specified, the results are not returned in the response.
    • RESULT_TABLE_PERSIST: If TRUE, then the result table specified in RESULT_TABLE will be persisted and will not expire unless a TTL is specified. If FALSE, then the result table will be an in-memory table and will expire unless a TTL is specified otherwise. Supported values:The default value is FALSE.
    • TTL: Sets the TTL of the table specified in RESULT_TABLE.
    The default value is an empty Map.
    Returns:
    Response object containing the results of the operation.
    Throws:
    GPUdbException - if an error occurs during the operation.