This function allows to perform consensus clustering using the k-means clustering algorithm, for a fixed number of clusters. We consider the number of clusters K to be fixed.

consensusCluster( data = NULL, K = 2, B = 100, pItem = 0.8, clMethod = "hclust", dist = "euclidean", hclustMethod = "average", sparseKmeansPenalty = NULL, maxIterKM = 1000 )

data | N X P data matrix |
---|---|

K | Number of clusters. |

B | Number of iterations. |

pItem | Proportion of items sampled at each iteration. |

clMethod | Clustering algorithm. Can be "hclust" for hierarchical clustering, "kmeans" for k-means clustering, "pam" for partitioning around medoids, "sparse-kmeans" for sparse k-means clustering or "sparse-hclust" for sparse hierarchical clustering. Default is "hclust". However, if the data contain at least one covariate that is a factor, the default clustering algorithm is "pam". |

dist | Distance used for hierarchical clustering. Can be "pearson" (for 1 - Pearson correlation), "spearman" (for 1- Spearman correlation), any of the distances provided in stats::dist() (i.e. "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski"), or a matrix containing the distances between the observations. |

hclustMethod | Hierarchical clustering method. Default is "average". For
more details see |

sparseKmeansPenalty | If the selected clustering method is "sparse-kmeans", this is the value of the parameter "wbounds" of the "KMeansSparseCluster" function. The default value is the square root of the number of variables. |

maxIterKM | Number of iterations for the k-means clustering algorithm. |

The output is a consensus matrix, that is a symmetric matrix where the element in position (i,j) corresponds to the proportion of times that items i and j have been clustered together.

Monti, S., Tamayo, P., Mesirov, J. and Golub, T., 2003. Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Machine learning, 52(1-2), pp.91-118.

Witten, D.M. and Tibshirani, R., 2010. A framework for feature selection in clustering. Journal of the American Statistical Association, 105(490), pp.713-726.

# Load one dataset with 300 observations, 2 variables, 6 clusters data <- as.matrix(read.csv(system.file("extdata", "dataset1.csv", package = "coca"), row.names = 1)) # Compute consensus clustering with K=5 clusters cm <- consensusCluster(data, K = 5)