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)
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