# hkmeans: Hierarchical k-means clustering In factoextra: Extract and Visualize the Results of Multivariate Data Analyses

## Description

The final k-means clustering solution is very sensitive to the initial random selection of cluster centers. This function provides a solution using an hybrid approach by combining the hierarchical clustering and the k-means methods. The procedure is explained in "Details" section. Read more: Hybrid hierarchical k-means clustering for optimizing clustering outputs.

• hkmeans(): compute hierarchical k-means clustering

• print.hkmeans(): prints the result of hkmeans

• hkmeans_tree(): plots the initial dendrogram

## Usage

 1 2 3 4 5 6 7 8 9 10 11 12 13 hkmeans( x, k, hc.metric = "euclidean", hc.method = "ward.D2", iter.max = 10, km.algorithm = "Hartigan-Wong" ) ## S3 method for class 'hkmeans' print(x, ...) hkmeans_tree(hkmeans, rect.col = NULL, ...)

## Arguments

 x a numeric matrix, data frame or vector k the number of clusters to be generated hc.metric the distance measure to be used. Possible values are "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski" (see ?dist). hc.method the agglomeration method to be used. Possible values include "ward.D", "ward.D2", "single", "complete", "average", "mcquitty", "median"or "centroid" (see ?hclust). iter.max the maximum number of iterations allowed for k-means. km.algorithm the algorithm to be used for kmeans (see ?kmeans). ... others arguments to be passed to the function plot.hclust(); (see ? plot.hclust) hkmeans an object of class hkmeans (returned by the function hkmeans()) rect.col Vector with border colors for the rectangles around clusters in dendrogram

## Details

The procedure is as follow:

1. Compute hierarchical clustering

2. Cut the tree in k-clusters

3. compute the center (i.e the mean) of each cluster

4. Do k-means by using the set of cluster centers (defined in step 3) as the initial cluster centers. Optimize the clustering.

This means that the final optimized partitioning obtained at step 4 might be different from the initial partitioning obtained at step 2. Consider mainly the result displayed by fviz_cluster().

## Value

hkmeans returns an object of class "hkmeans" containing the following components:

• The elements returned by the standard function kmeans() (see ?kmeans)

• data: the data used for the analysis

• hclust: an object of class "hclust" generated by the function hclust()

## Examples

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 # Load data data(USArrests) # Scale the data df <- scale(USArrests) # Compute hierarchical k-means clustering res.hk <-hkmeans(df, 4) # Elements returned by hkmeans() names(res.hk) # Print the results res.hk # Visualize the tree hkmeans_tree(res.hk, cex = 0.6) # or use this fviz_dend(res.hk, cex = 0.6) # Visualize the hkmeans final clusters fviz_cluster(res.hk, frame.type = "norm", frame.level = 0.68)

factoextra documentation built on April 2, 2020, 1:09 a.m.