Description Usage Arguments Value Author(s) References Examples

Computes the consensus between Ward's minimum variance and Complete-linkage (or Single-linkage) algorithms (i.e., the number of elements classified together by both algorithms).

1 | ```
consensus_cluster(k, cluster_ward, cluster_other)
``` |

`k` |
[int] a vector containing the number of clusters for Ward and for Complete-linkage (or Single-linkage) algorithms, respectively |

`cluster_ward` |
an object of class hclust for the Ward algorithm |

`cluster_other` |
an object of class hclust for the Complete-linkage (or Single-linkage) algorithm |

an object of class `consensus_cluster`

with the following
elements:

`elements` |
list of the elements belonging to each cluster |

;

`A_star` |
contingency table of the clustering |

;

`max_consensus` |
maximum clustering consensus |

.

Paola Tellaroli, <paola [dot] tellaroli [at] unipd [dot] it>;; Marco Bazzi, <bazzi [at] stat [dot] unipd [dot] it>; Michele Donato, <mdonato [at] stanford [dot] edu>.

Tellaroli P, Bazzi M., Donato M., Brazzale A. R., Draghici S. (2016). Cross-Clustering: A Partial Clustering Algorithm with Automatic Estimation of the Number of Clusters. PLoS ONE 11(3): e0152333. doi:10.1371/journal.pone.0152333

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
library(CrossClustering)
data(toy)
### toy is transposed as we want to cluster samples (columns of the
### original matrix)
toy_dist <- t(toy) %>% dist(method = "euclidean")
### Hierarchical clustering
cluster_ward <- toy_dist %>% hclust(method = "ward.D")
cluster_other <- toy_dist %>% hclust(method = "complete")
### consensus_cluster
CrossClustering:::consensus_cluster(c(3, 4),
cluster_ward,
cluster_other
)
``` |

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