Automated K-Means Clustering + PCA/t-SNE

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 = NULL,
  wss_var = 0,
  limit = 15,
  drop_na = TRUE,
  ignore = NULL,
  ohse = TRUE,
  norm = TRUE,
  algorithm = c("Hartigan-Wong", "Lloyd", "Forgy", "MacQueen"),
  dim_red = "PCA",
  comb = c(1, 2),
  seed = 123,
  quiet = FALSE,
  ...
)

Arguments

df

Dataframe

k

Integer. Number of clusters

wss_var

Numeric. Used to pick automatic k value, when k is NULL based on WSS variance while considering limit clusters. Values between (0, 1). Default value could be 0.05 to consider convergence.

limit

Integer. How many clusters should be considered?

drop_na

Boolean. Should NA rows be removed?

ignore

Character vector. Names of columns to ignore.

ohse

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

norm

Boolean. Should the data be normalized?

algorithm

character: may be abbreviated. Note that "Lloyd" and "Forgy" are alternative names for one algorithm.

dim_red

Character. Select dimensionality reduction technique. Pass any of: c("PCA", "tSNE", "all", "none").

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.

...

Additional parameters to pass sub-functions.

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 (when dim_red="PCA")

• tSNE list with t-SNE results (when dim_red="tSNE")

See also

Other Clusters: clusterOptimalK(), clusterVisualK(), reduce_pca(), reduce_tsne()

Examples

Sys.unsetenv("LARES_FONT") # Temporal
data("iris")
df <- subset(iris, select = c(-Species))

# If dataset has +5 columns, feel free to reduce dimenstionalities
# with reduce_pca() or reduce_tsne() first

# Find optimal k
check_k <- clusterKmeans(df, limit = 10)
#> >>> Removed duplicate obserations: 1
check_k$nclusters_plot

# Or pick k automatically based on WSS variance
check_k <- clusterKmeans(df, wss_var = 0.05, limit = 10)
#> >>> Removed duplicate obserations: 1
#> >> Auto selected k = 3 (clusters) based on minimum WSS variance of 5%
# You can also use our other functions:
# clusterOptimalK(df) and clusterVisualK(df)

# Run with selected k
clusters <- clusterKmeans(df, k = 3)
#> >>> Removed duplicate obserations: 1
names(clusters)
#> [1] "nclusters"      "nclusters_plot" "df"             "clusters"      
#> [5] "fit"            "means"          "correlations"   "PCA"           

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


# PCA Results (when dim_red = "PCA")
plot(clusters$PCA$plot_explained)

plot(clusters$PCA$plot)