R: Visual enhancement of clustering analysis

eclust {factoextra} | R Documentation

## Visual enhancement of clustering analysis

### Description

Provides solution for enhancing the workflow of clustering analyses and ggplot2-based elegant data visualization. Read more: Visual enhancement of clustering analysis.

### Usage

    eclust(x, FUNcluster = c("kmeans", "pam", "clara", "fanny", "hclust", "agnes",
      "diana"), k = NULL, k.max = 10, stand = FALSE, graph = TRUE,
      hc_metric = "euclidean", hc_method = "ward.D2", gap_maxSE = list(method
      = "firstSEmax", SE.factor = 1), nboot = 100, verbose = interactive(),
      seed = 123, ...)

### Arguments

`x` | numeric vector, data matrix or data frame
`FUNcluster` | a clustering function including "kmeans", "pam", "clara", "fanny", "hclust", "agnes" and "diana". Abbreviation is allowed.
`k` | the number of clusters to be generated. If NULL, the gap statistic is used to estimate the appropriate number of clusters. In the case of kmeans, k can be either the number of clusters, or a set of initial (distinct) cluster centers.
`k.max` | the maximum number of clusters to consider, must be at least two.
`stand` | logical value; default is FALSE. If TRUE, then the data will be standardized using the function scale(). Measurements are standardized for each variable (column), by subtracting the variable's mean value and dividing by the variable's standard deviation.
`graph` | logical value. If TRUE, cluster plot is displayed.
`hc_metric` | character string specifying the metric to be used for calculating dissimilarities between observations. Allowed values are those accepted by the function dist() [including "euclidean", "manhattan", "maximum", "canberra", "binary", "minkowski"] and correlation based distance measures ["pearson", "spearman" or "kendall"]. Used only when FUNcluster is a hierarchical clustering function such as one of "hclust", "agnes" or "diana".
`hc_method` | the agglomeration method to be used (?hclust): "ward.D", "ward.D2", "single", "complete", "average", ...
`gap_maxSE` | a list containing the parameters (method and SE.factor) for determining the location of the maximum of the gap statistic (Read the documentation ?cluster::maxSE).
`nboot` | integer, number of Monte Carlo ("bootstrap") samples. Used only for determining the number of clusters using gap statistic.
`verbose` | logical value. If TRUE, the result of progress is printed.
`seed` | integer used for seeding the random number generator.
`...` | other arguments to be passed to FUNcluster.

### Value

Returns an object of class "eclust" containing the result of the standard function used (e.g., kmeans, pam, hclust, agnes, diana, etc.).

It includes also:

- cluster: the cluster assignement of observations after cutting the tree

- nbclust: the number of clusters

- silinfo: the silhouette information of observations, including $widths (silhouette width values of each observation), $clus.avg.widths (average silhouette width of each cluster) and $avg.width (average width of all clusters)

- size: the size of clusters

- data: a matrix containing the original or the standardized data (if stand = TRUE)

The "eclust" class has method for fviz_silhouette(), fviz_dend(), fviz_cluster().

### Author(s)

Alboukadel Kassambara alboukadel.kassambara@gmail.com

### See Also

`fviz_silhouette`, `fviz_dend`, `fviz_cluster`

### Examples

    # Load and scale data
    data("USArrests")
    df <- scale(USArrests)

    # Enhanced k-means clustering
    # nboot >= 500 is recommended
    res.km <- eclust(df, "kmeans", nboot = 2)
    # Silhouette plot
    fviz_silhouette(res.km)
    # Optimal number of clusters using gap statistics
    res.km$nbclust
    # Print result
     res.km

    ## Not run:
     # Enhanced hierarchical clustering
     res.hc <- eclust(df, "hclust", nboot = 2) # compute hclust
      fviz_dend(res.hc) # dendrogam
      fviz_silhouette(res.hc) # silhouette plot

    ## End(Not run)

[Package factoextra version 1.0.5 Index]