Kmeans Clustering of Penguins

Method 1 – Visual Inspection

The most blunt method is to visualize cluster data for assorted values of k.

I will be largely using the factoextra package by Alboukadel Kassambara and Fabian Mundt. The package contains a wide array of clustering algorithms and visualizations, along with tools to identify the optimal number of clusters. Particulary helpful is factoextra’s clustering visualizations output in ggplot format, which simplifies further chart adjustments by leveraging the standard ggplot functions.

Fviz_cluster is a useful function to visualize clusters for a given k. The function creates a scatterplot with points in a cluster color-coordinated and encircled with a polygram. Clustering on greater than two fields is difficult to visualize so fields are helpfully automatically converted to two dimensions via principal component analysis (PCA).

Multiple fviz_cluster visualizations can be easily created in R. First, the factoextra::fviz_cluster function creates the chart for one K. Second, the functdional purrr:map creates charts for multiple instaces of K. Finally, patchwork::patchwork plots the resulting charts into a common visualization. A traditional starting point for k values is 1 to 9.

   library(patchwork)
   library(glue)
   library(here)

   kmeans_flex <- function (k) {
      penguins_kmeans <- kmeans(penguins[5:7], k) 
      fviz_cluster(penguins_kmeans, geom = "point", data = penguins[5:7]) +
      labs(title = glue("{k} clusters")) +
      theme (
         plot.background = element_blank(),
         panel.background = element_blank(),plot.title = element_text (margin = margin(0,0,5,0), hjust = 0.5, size = 12, color = "grey", family = "Lato"),
         legend.text = element_text(hjust = 0, size = 8, family = "Lato"),
         legend.position = "none",
         legend.title = element_text(size = 8),
         axis.title = element_text (size = 8),
         axis.text = element_text (size = 8)
      )
      }

   cluster_possibles <- map (1:9, kmeans_flex)
   
   cluster_possibles[[1]] + cluster_possibles[[2]] + cluster_possibles[[3]] +
      cluster_possibles[[4]] + cluster_possibles[[5]] + cluster_possibles[[6]] +
      cluster_possibles[[7]] + cluster_possibles[[8]] + cluster_possibles[[9]] +
      plot_annotation (
         title = "Kmeans Clustering of Penguins across potential number of clusters \U0022k\U0022 ",
         caption = "Visualization: Joel Soroos @soroosj  |  Data: R palmerpenguins package via R4DS Tidy Tuesday",
         theme = theme (
            plot.title = element_text(hjust = 0.5, vjust = 0.5, size = 14, face = "bold", margin = margin (0,0,20,0)),
            plot.caption = element_text (hjust = 1, size = 7, margin = margin (15,0,0,0)) 
            )
         )

Results indicate a significant white space in middle of the chart so clearly a k of 1 is too small; therefore, clustering will add insight. Two or three clusters look promising as minimal overlap. Clusters greater than three have significant overlap so seem less optimal.

The visualizations did not provide a clear answer whether a cluster size of 2 or 3 is optimal. We need to proceed to more sophisticated methodologies.

The factoextra:fviz_nbclust function provides assorted methodologies to determine the optimal K. I calculated results for all three methodologies using another functional loop.

   methodologies <- c("wss", "silhouette", "gap_stat")
   
   cluster_optimal <- map (methodologies, ~fviz_nbclust (penguins[5:7], kmeans, method = .x))

Method 2 – Elbow

Optimal clusters are at the point in which the knee "bends" or in mathemetical terms when the marginal total within sum of squares ("wss") for an additional cluster begins to decrease at a linear rate. Similar to the visualization method, the results are subjective.

   cluster_optimal[[1]]

There are significant inflections at both 2 at 3 clusters. We can rule out an optimal number of clusters above 3 because minimal marginal reduction in total within sum of squares. However, the model is ambiguous on whether 2 or 3 clusters is optimal.

Method 3 – Silhouette

The silhouette value indicates the quality of the clustering. similarity of a data point to its own cluster compared to other clusters. A silhoutte width nearer to 1 indicates the point is well-matched to its cluster and poorly matched to neighboring clusters. Silhouette widths approaching -1 are better matched to neighboring clusters.

   cluster_optimal[[2]]

The average silhouette length begins to decrease after 2 clusters. Accordingly the recommendation here is k = 2.

Method 4 – Gap Statistic

The gap statistic test is a newer optimal K test by Robert Tisharni, Guenther Walther and Trevor Hastie. The methodology compares the total within intra-cluster variation ("WSS") for different values of k relative to a random uniform distribution of the data (that has no obvious clustering). The optimal cluster value of k has the largest gap statistic because signifies the cluster infrastructure furthest from the random uniform point distribution.

   cluster_optimal[[3]]

The gap statistic test calls for a cluster size (k) of 3.

Method 5: Multiple indexes

The NbClust package by Malika Charrad, Nadia Ghazzali and Azam Niknafs calculates the optimal K using 30 methodologies and aggregates into an index. The package equal-weightes each methodology and presents results in a histogram. The suggested optimal k is the k with the most optimizations across the 30 index methodologies.

   library (NbClust)

   cluster_30_indexes <- NbClust(data = penguins[5:7], distance = "euclidean", min.nc = 2, max.nc = 9, method = "complete", index ="all")

## *** : The Hubert index is a graphical method of determining the number of clusters.
##                 In the plot of Hubert index, we seek a significant knee that corresponds to a 
##                 significant increase of the value of the measure i.e the significant peak in Hubert
##                 index second differences plot. 
## 

## *** : The D index is a graphical method of determining the number of clusters. 
##                 In the plot of D index, we seek a significant knee (the significant peak in Dindex
##                 second differences plot) that corresponds to a significant increase of the value of
##                 the measure. 
##  
## ******************************************************************* 
## * Among all indices:                                                
## * 2 proposed 2 as the best number of clusters 
## * 16 proposed 3 as the best number of clusters 
## * 1 proposed 4 as the best number of clusters 
## * 2 proposed 6 as the best number of clusters 
## * 1 proposed 8 as the best number of clusters 
## * 1 proposed 9 as the best number of clusters 
## 
##                    ***** Conclusion *****                            
##  
## * According to the majority rule, the best number of clusters is  3 
##  
##  
## *******************************************************************
   fviz_nbclust(cluster_30_indexes) +
      theme_minimal() +
      labs(title = "Frequency of Optimal Clusters using 30 indexes in NbClust Package")
## Among all indices: 
## ===================
## * 2 proposed  0 as the best number of clusters
## * 1 proposed  1 as the best number of clusters
## * 2 proposed  2 as the best number of clusters
## * 16 proposed  3 as the best number of clusters
## * 1 proposed  4 as the best number of clusters
## * 2 proposed  6 as the best number of clusters
## * 1 proposed  8 as the best number of clusters
## * 1 proposed  9 as the best number of clusters
## 
## Conclusion
## =========================
## * According to the majority rule, the best number of clusters is  3 .

The 30 indexes seem to suggest 3 is the optimal number of clusters.