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`user_points`

  • Available in: K-Means
  • Hyperparameter: no

Description​

This option allows you to specify a dataframe, where each row represents an initial cluster center.

Notes:

  • The user-specified points must have the same number of columns as the training observations.
  • The number of rows must equal the number of clusters.
  • init=furthest by default. However, if a user-points file is specified and a value for init is not, then init will automatically change to user.

Example​

library(h2o)
h2o.init()

# import the iris dataset:
# this dataset is used to classify the type of iris plant
# the original dataset can be found at https://archive.ics.uci.edu/ml/datasets/Iris
iris <- h2o.importFile("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv")

# convert response column to a factor
iris['class'] <- as.factor(iris['class'])

# set the predictor names 
predictors <- colnames(iris)[-length(iris)]

# split into train and validation
iris_splits <- h2o.splitFrame(data = iris, ratios = 0.8, seed = 1234)
train <- iris_splits[[1]]
valid <- iris_splits[[2]]

# specify your points
point1 <- c(4.9, 3.0, 1.4, 0.2)
point2 <- c(5.6, 2.5, 3.9, 1.1)
point3 <- c(6.5, 3.0, 5.2, 2.0)

# create an H2OFrame with your points
points <- as.h2o(t(data.frame(point1, point2, point3)))

# take a look at the H2OFrame
print(points)

# try using the `user_points` parameter:
iris_kmeans <- h2o.kmeans(x = predictors, k = 3, user_points =  points, training_frame = train, validation_frame = valid, seed = 1234)

# print the total within cluster sum-of-square error for the validation dataset
print(paste0("Total sum-of-square error for valid dataset: ", h2o.tot_withinss(object = iris_kmeans, valid = T)))
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