TD_KMeans

fastpath function to generate clustering model containing cluster centroids using KMeans algorithm.

Options

IdColumn: Specifies the column which is unique identifier of input row.
MaxIterNum: Specify the maximum number of iterations that the algorithm runs before quitting if the convergence threshold has not been met.
NumClusters: Specifies the number of clusters to be produced. This argument is not allowed with InitialCentroidsTable provided.
NumInit: The number of times, the k-means algorithm will be run with different initial centroid seeds. The function will emit out the model having the least value of Total Within Cluster Squared Sum.
OutputClusterAssignment: Specifies whether to output Cluster Assignment.
Seed: Specify the random seed the algorithm uses for repeatable results. The algorithm uses the seed to randomly sample the input table rows as initial clusters.
StopThreshold: Specify the convergence threshold. When the centroids move by less than this amount, the algorithm has converged.
Output Schema: Output Schema, if Volatile is true then use user login as the schema.
Output Table: Output Table
VAL Location: VAL Location
TargetColumns: Specifies the columns/features to be used to cluster the data.
Volatile: Specifies whether the table should be a VOLATILE table. If true, then the table is automatically deleted, otherwise it is users responsibility to remove or clean it up for space.