View source: R/stabilityIndex.R
| stability | R Documentation | 
This analysis permits to estimate whether the clustering is meaningfully
affected by small variations in the sample. First, a clustering using the
k-means algorithm is carried out. The value of k can be provided by the user.
Then, the stability index is the mean of the Jaccard coefficient
values of a number of bs bootstrap replicates. The values are in the range [0,1],
having the following meaning:
Unstable: [0, 0.60[.
Doubtful: [0.60, 0.75].
Stable: ]0.75, 0.85].
Highly Stable: ]0.85, 1].
stability(
  data,
  k = 5,
  bs = 100,
  cbi = "kmeans",
  getImages = FALSE,
  all_metrics = FALSE,
  seed = NULL,
  gold_standard = NULL,
  ...
)
| data | A  | 
| k | Positive integer. Number of clusters between [2,15] range. | 
| bs | Positive integer. Bootstrap value to perform the resampling. | 
| cbi | Clusterboot interface name (default: "kmeans"):
"kmeans", "clara", "clara_pam", "hclust", "pamk", "pamk_pam", "pamk".
Any CBI appended with '_pam' makes use of  | 
| getImages | Boolean. If true, a plot is displayed. | 
| all_metrics | Boolean. If true, clustering is performed upon all the dataset. | 
| seed | Positive integer. A seed for internal bootstrap. | 
| gold_standard: | Numeric vector. A vector of clusters from a gold standard classification, e.g. c(1,2,1,1,2). Only applicable if parameter 'all_metrics' is set to TRUE. | 
A ExperimentList containing the stability and cluster measurements
for k clusters.
milligan1996measuringevaluomeR
\insertRefjaccard1901distributionevaluomeR
# Using example data from our package
data("ontMetrics")
result <- stability(ontMetrics, k=6, getImages=TRUE)
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