Clustering evaluation through stability
Evaluation a clustering algorithm according to stability, through a bootstrap procedure.
stability(
clusteringmethods,
d,
originals = NULL,
eval = "jaccard",
type = c("cluster", "global"),
nsampling = 10,
seed = NULL,
names = NULL,
graph = FALSE,
...
)clusteringmethods |
The clustering methods to be evaluated. |
d |
The dataset. |
originals |
The original clustering. |
eval |
The evaluation criteria. |
type |
The comparison method. |
nsampling |
The number of bootstrap runs. |
seed |
A specified seed for random number generation (useful for testing different method with the same bootstap samplings). |
names |
Method names. |
graph |
Indicates wether or not a graphic is potted for each sample. |
... |
Parameters to be passed to the clustering algorithms. |
The evaluation of the clustering algorithm(s) (numeric values).
## Not run:
require (datasets)
data (iris)
stability (KMEANS, iris [, -5], seed = 0, k = 3)
stability (KMEANS, iris [, -5], seed = 0, k = 3, eval = c ("jaccard", "accuracy"), type = "global")
stability (KMEANS, iris [, -5], seed = 0, k = 3, type = "cluster")
stability (KMEANS, iris [, -5], seed = 0, k = 3, eval = c ("jaccard", "accuracy"), type = "cluster")
stability (c (KMEANS, HCA), iris [, -5], seed = 0, k = 3)
stability (c (KMEANS, HCA), iris [, -5], seed = 0, k = 3,
eval = c ("jaccard", "accuracy"), type = "global")
stability (c (KMEANS, HCA), iris [, -5], seed = 0, k = 3, type = "cluster")
stability (c (KMEANS, HCA), iris [, -5], seed = 0, k = 3,
eval = c ("jaccard", "accuracy"), type = "cluster")
stability (KMEANS, iris [, -5], originals = KMEANS (iris [, -5], k = 3)$cluster, seed = 0, k = 3)
stability (KMEANS, iris [, -5], originals = KMEANS (iris [, -5], k = 3), seed = 0, k = 3)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.