Make Predictions from a "UnifiedClustering" Object

Similar to other predict methods, this function Cluster assignment is a unified interface to call a cluster assignment algorithm to assign data to clusters that are previously generated by some clustering methods, including K-Means, Accelerated K-Means, K-Medians, K-Medoids, DBSCAN, SOM, and GMM. AgglomerateHierarchicalClustering does not provide predict function!

# S3 method for UnifiedClustering
predict(model, data, key, features = NULL, func = NULL)

Arguments

model	R6Class A "UnifiedClustering" object for prediction.
data	DataFrame DataFrame containting the data.
key	character Name of the ID column.
features	character of list of characters, optional Name of feature columns for prediction. If not provided, it defaults to all non-key columns of data.
func	character, optional The functionality for unified Clustering model. Mandatory only when the func attribute of model is NULL. Valid values are as follows: "AgglomerateHierarchicalClustering", "DBSCAN", "GaussianMixture", "AcceleratedKMeans", "KMeans", "KMedians", "KMedoids", "SOM".

Format

S3 methods

Value

Predicted values are returned as a DataFrame, structured as follows.

• ID column name.

• CLUSTER_ID: Assigned cluster ID.

• DISTANCE: Distance metric between a given point and the assigned cluster.

Examples

Input data for prediction:

> df.predict$Collect()
   ID  CLUSTER_ID  DISTANCE
1  88           3  0.981659
2  89           3  0.826454
3  90           2  1.990205
4  91           2  0.325812

Call the predict() function:

> res <- predict(model = ukmeans,
                 data = df.predict,
                 key = "ID",
                 func = "KMeans")

Check the result:

> res$Collect()
   ID  CLUSTER_ID  DISTANCE
1  88           3  0.981659
2  89           3  0.826454
3  90           2  1.990205
4  91           2  0.325812

See also

hanaml.UnifiedClustering