lares (version 4.10.6)

clusterKmeans: Automated K-Means Clustering + PCA

Description

This function lets the user cluster a whole data.frame automatically. As you might know, the goal of kmeans is to group data points into distinct non-overlapping subgroups. If needed, one hot encoding will be applied to categorical values automatically with this function. For consideration: Scale/standardize the data when applying kmeans. Also, kmeans assumes spherical shapes of clusters and does not work well when clusters are in different shapes such as elliptical clusters.

Usage

clusterKmeans(
  df,
  k = NA,
  limit = 20,
  drop_na = TRUE,
  ignore = NA,
  ohse = TRUE,
  norm = TRUE,
  comb = c(1, 2),
  seed = 123,
  quiet = TRUE
)

Arguments

df

Dataframe

k

Integer. Number of clusters

limit

Integer. How many clusters should be considered?

drop_na

Boolean. Should NA rows be removed?

ignore

Character vector. Which columns should be excluded when calculating kmeans?

ohse

Boolean. Do you wish to automatically run one hot encoding to non-numerical columns?

norm

Boolean. Should the data be normalized?

comb

Vector. Which columns do you wish to plot? Select which two variables by name or column position.

seed

Numeric. Seed for reproducibility

quiet

Boolean. Keep quiet? If not, print messages.

Value

List. If no k is provided, contains nclusters and nclusters_plot to determine optimal k given their WSS (Within Groups Sum of Squares). If k is provided, additionally we get:

• df data.frame with original df plus cluster column

• clusters integer which is the same as k

• fit kmeans object used to fit clusters

• means data.frame with means and counts for each cluster

• correlations plot with correlations grouped by clusters

• PCA list with PCA results

See Also

Other Clusters: clusterOptimalK(), clusterVisualK()

Examples

# NOT RUN {
Sys.unsetenv("LARES_FONT") # Temporal
data("iris")
df <- subset(iris, select = c(-Species))

# Find optimal k
check_k <- clusterKmeans(df, limit = 10)
check_k$nclusters_plot
# You can also use our other functions:
# clusterOptimalK() and clusterVisualK()

# Run with selected k
clusters <- clusterKmeans(df, k = 3)
names(clusters)

# Cross-Correlations for each cluster
plot(clusters$correlations)

# PCA Results
plot(clusters$PCA$plotVarExp)
plot(clusters$PCA$plot_1_2)

# You must have \code{ggforce} library to use this auxiliary function:
# 3D interactive plot
# }
# NOT RUN {
clusters$PCA$plot_1_2_3
# }