ensemble: Generate an ensemble of partitions.

Description Usage Arguments Value Examples

Description

Generate multiple clustering results (that is, partitions) based on multiple versions of perturbed data using a specified baseline clustering method.

Usage

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ensemble(data, nbs, clust_param, clustering = "kmeans",
  perturb_method = 1)

Arguments

data

– data that will be perturbed.

nbs

– the number of clustering partitions to be generated.

clust_param

– parameters for pre-defined clustering methods. If clustering is "kmeans", "Mclust", "hclust", this is an integer indicating the number of clusters. For "dbscan", a numeric indicating epsilon. For "HMM-VB", a list of parameters.

clustering

– baseline clustering methods. User specified functions or example methods included in package ("kmeans", "Mclust", "hclust", "dbscan", "PCAreduce", "HMM-VB") can be used. Refer to the Detail.

perturb_method

– adding noise is 0 and bootstrap resampling is 1. Default is bootstrap resampling. # perturb_method=0 perturbed by adding Gaussian noise.

Value

a matrix of cluster labels of the ensemble partitions. Each column is cluster labels of an individual clustering result.

Examples

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data(sim1)
# the number of clusters.
C = 4
ens.data = ensemble(sim1$X[1:10,], nbs=10, clust_param=C, clustering="kmeans", perturb_method=1)

OTclust documentation built on May 6, 2019, 9 a.m.