Format cluster models obtained for example by kmeans().
Usage
# S3 method for class 'hclust'
model_parameters(model, data = NULL, clusters = NULL, ...)Examples
# \donttest{
#
# K-means -------------------------------
model <- kmeans(iris[1:4], centers = 3)
rez <- model_parameters(model)
rez
#> # Clustering Solution
#>
#> The 3 clusters accounted for 88.43% of the total variance of the original data.
#>
#> Cluster | n_Obs | Sum_Squares | Sepal.Length | Sepal.Width | Petal.Length | Petal.Width
#> ---------------------------------------------------------------------------------------
#> 1 | 38 | 23.88 | 6.85 | 3.07 | 5.74 | 2.07
#> 2 | 62 | 39.82 | 5.90 | 2.75 | 4.39 | 1.43
#> 3 | 50 | 15.15 | 5.01 | 3.43 | 1.46 | 0.25
# Get clusters
predict(rez)
#> [1] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#> [38] 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [75] 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 1 1 1 2 1 1 1 1
#> [112] 1 1 2 2 1 1 1 1 2 1 2 1 2 1 1 2 2 1 1 1 1 1 2 1 1 1 1 2 1 1 1 2 1 1 1 2 1
#> [149] 1 2
# Clusters centers in long form
attributes(rez)$means
#> Cluster n_Obs Sum_Squares Variable Mean
#> 1 1 38 23.87947 Sepal.Length 6.850000
#> 2 1 38 23.87947 Sepal.Width 3.073684
#> 3 1 38 23.87947 Petal.Length 5.742105
#> 4 1 38 23.87947 Petal.Width 2.071053
#> 5 2 62 39.82097 Sepal.Length 5.901613
#> 6 2 62 39.82097 Sepal.Width 2.748387
#> 7 2 62 39.82097 Petal.Length 4.393548
#> 8 2 62 39.82097 Petal.Width 1.433871
#> 9 3 50 15.15100 Sepal.Length 5.006000
#> 10 3 50 15.15100 Sepal.Width 3.428000
#> 11 3 50 15.15100 Petal.Length 1.462000
#> 12 3 50 15.15100 Petal.Width 0.246000
# Between and Total Sum of Squares
attributes(rez)$Sum_Squares_Total
#> [1] 681.3706
attributes(rez)$Sum_Squares_Between
#> [1] 602.5192
#
# Hierarchical clustering (hclust) ---------------------------
data <- iris[1:4]
model <- hclust(dist(data))
clusters <- cutree(model, 3)
rez <- model_parameters(model, data, clusters)
rez
#> # Clustering Solution
#>
#> The 3 clusters accounted for 86.86% of the total variance of the original data.
#>
#> Cluster | n_Obs | Sum_Squares | Sepal.Length | Sepal.Width | Petal.Length | Petal.Width
#> ---------------------------------------------------------------------------------------
#> 1 | 50 | 15.15 | 5.01 | 3.43 | 1.46 | 0.25
#> 2 | 72 | 64.62 | 6.55 | 2.96 | 5.27 | 1.85
#> 3 | 28 | 9.75 | 5.53 | 2.64 | 3.96 | 1.23
# Get clusters
predict(rez)
#> [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 3 2 3 2 3 2 3 3 3 3 2 3 2 3 3 2 3 2 3 2 2
#> [75] 2 2 2 2 2 3 3 3 3 2 3 2 2 2 3 3 3 2 3 3 3 3 3 2 3 3 2 2 2 2 2 2 3 2 2 2 2
#> [112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [149] 2 2
# Clusters centers in long form
attributes(rez)$means
#> Cluster n_Obs Sum_Squares Variable Mean
#> 1 1 50 15.151000 Sepal.Length 5.006000
#> 2 1 50 15.151000 Sepal.Width 3.428000
#> 3 1 50 15.151000 Petal.Length 1.462000
#> 4 1 50 15.151000 Petal.Width 0.246000
#> 5 2 72 64.624722 Sepal.Length 6.545833
#> 6 2 72 64.624722 Sepal.Width 2.963889
#> 7 2 72 64.624722 Petal.Length 5.273611
#> 8 2 72 64.624722 Petal.Width 1.850000
#> 9 3 28 9.749286 Sepal.Length 5.532143
#> 10 3 28 9.749286 Sepal.Width 2.635714
#> 11 3 28 9.749286 Petal.Length 3.960714
#> 12 3 28 9.749286 Petal.Width 1.228571
# Between and Total Sum of Squares
attributes(rez)$Total_Sum_Squares
#> NULL
attributes(rez)$Between_Sum_Squares
#> NULL
#
# Hierarchical K-means (factoextra::hkclust) ----------------------
data <- iris[1:4]
model <- factoextra::hkmeans(data, k = 3)
rez <- model_parameters(model)
rez
#> # Clustering Solution
#>
#> The 3 clusters accounted for 88.43% of the total variance of the original data.
#>
#> Cluster | n_Obs | Sum_Squares | Sepal.Length | Sepal.Width | Petal.Length | Petal.Width
#> ---------------------------------------------------------------------------------------
#> 1 | 50 | 15.15 | 5.01 | 3.43 | 1.46 | 0.25
#> 2 | 62 | 39.82 | 5.90 | 2.75 | 4.39 | 1.43
#> 3 | 38 | 23.88 | 6.85 | 3.07 | 5.74 | 2.07
# Get clusters
predict(rez)
#> [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [75] 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 3 3 3 3 2 3 3 3 3
#> [112] 3 3 2 2 3 3 3 3 2 3 2 3 2 3 3 2 2 3 3 3 3 3 2 3 3 3 3 2 3 3 3 2 3 3 3 2 3
#> [149] 3 2
# Clusters centers in long form
attributes(rez)$means
#> Cluster n_Obs Sum_Squares Variable Mean
#> 1 1 50 15.15100 Sepal.Length 5.006000
#> 2 1 50 15.15100 Sepal.Width 3.428000
#> 3 1 50 15.15100 Petal.Length 1.462000
#> 4 1 50 15.15100 Petal.Width 0.246000
#> 5 2 62 39.82097 Sepal.Length 5.901613
#> 6 2 62 39.82097 Sepal.Width 2.748387
#> 7 2 62 39.82097 Petal.Length 4.393548
#> 8 2 62 39.82097 Petal.Width 1.433871
#> 9 3 38 23.87947 Sepal.Length 6.850000
#> 10 3 38 23.87947 Sepal.Width 3.073684
#> 11 3 38 23.87947 Petal.Length 5.742105
#> 12 3 38 23.87947 Petal.Width 2.071053
# Between and Total Sum of Squares
attributes(rez)$Sum_Squares_Total
#> [1] 681.3706
attributes(rez)$Sum_Squares_Between
#> [1] 602.5192
# K-Medoids (PAM and HPAM) ==============
model <- cluster::pam(iris[1:4], k = 3)
model_parameters(model)
#> # Clustering Solution
#>
#> The 3 clusters accounted for 88.43% of the total variance of the original data.
#>
#> Cluster | n_Obs | Sum_Squares | Sepal.Length | Sepal.Width | Petal.Length | Petal.Width
#> ---------------------------------------------------------------------------------------
#> 1 | 50 | 15.15 | 5.01 | 3.43 | 1.46 | 0.25
#> 2 | 62 | 39.82 | 5.90 | 2.75 | 4.39 | 1.43
#> 3 | 38 | 23.88 | 6.85 | 3.07 | 5.74 | 2.07
model <- fpc::pamk(iris[1:4], criterion = "ch")
model_parameters(model)
#> # Clustering Solution
#>
#> The 3 clusters accounted for 88.43% of the total variance of the original data.
#>
#> Cluster | n_Obs | Sum_Squares | Sepal.Length | Sepal.Width | Petal.Length | Petal.Width
#> ---------------------------------------------------------------------------------------
#> 1 | 50 | 15.15 | 5.01 | 3.43 | 1.46 | 0.25
#> 2 | 62 | 39.82 | 5.90 | 2.75 | 4.39 | 1.43
#> 3 | 38 | 23.88 | 6.85 | 3.07 | 5.74 | 2.07
# DBSCAN ---------------------------
model <- dbscan::dbscan(iris[1:4], eps = 1.45, minPts = 10)
rez <- model_parameters(model, iris[1:4])
rez
#> # Clustering Solution
#>
#> The 2 clusters accounted for 77.26% of the total variance of the original data.
#>
#> Cluster | n_Obs | Sum_Squares | Sepal.Length | Sepal.Width | Petal.Length | Petal.Width
#> ---------------------------------------------------------------------------------------
#> 1 | 50 | 15.15 | 5.01 | 3.43 | 1.46 | 0.25
#> 2 | 100 | 139.80 | 6.26 | 2.87 | 4.91 | 1.68
# Get clusters
predict(rez)
#> NULL
# Clusters centers in long form
attributes(rez)$means
#> Cluster n_Obs Sum_Squares Variable Mean
#> 1 1 50 15.151 Sepal.Length 5.006
#> 2 1 50 15.151 Sepal.Width 3.428
#> 3 1 50 15.151 Petal.Length 1.462
#> 4 1 50 15.151 Petal.Width 0.246
#> 5 2 100 139.796 Sepal.Length 6.262
#> 6 2 100 139.796 Sepal.Width 2.872
#> 7 2 100 139.796 Petal.Length 4.906
#> 8 2 100 139.796 Petal.Width 1.676
# Between and Total Sum of Squares
attributes(rez)$Sum_Squares_Total
#> [1] 681.3706
attributes(rez)$Sum_Squares_Between
#> [1] 526.4236
# HDBSCAN
model <- dbscan::hdbscan(iris[1:4], minPts = 10)
model_parameters(model, iris[1:4])
#> # Clustering Solution
#>
#> The 2 clusters accounted for 77.26% of the total variance of the original data.
#>
#> Cluster | n_Obs | Sum_Squares | Sepal.Length | Sepal.Width | Petal.Length | Petal.Width
#> ---------------------------------------------------------------------------------------
#> 1 | 100 | 139.80 | 6.26 | 2.87 | 4.91 | 1.68
#> 2 | 50 | 15.15 | 5.01 | 3.43 | 1.46 | 0.25
# }