mclean: Multilevel clustering exploration as network
In danielalcaide/mclean: Multilevel Clustering Exploration As Network

Description Usage Arguments Value Author(s) References Examples

MCLEAN performs hierarchical clustering and transforms the output into a network.

1 2	mclean(distance_matrix = NULL, threshold = NULL, method = "single", tree = NULL, node_simplification = TRUE, ...)

`distance_matrix`	a dissimilarity structure as produced by dist.
`threshold`	numeric scalar cutoff used to cluster elements. It is equivalent to the height in the dendrogram.
`method`	the agglomeration method to be used. This should be (an unambiguous abbreviation of) one of "ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or "centroid" (= UPGMC).
`tree`	a tree as produced by hclust. If the tree is not provided, the function generates it automatically through hclust() function.
`node_simplification`	logical value to disable the node aggregation. The default is TRUE.

It returns a list containing the following elements:

nodes: a data frame with the definition of the nodes. The variable "name is the node identifier used internally; nodes is best candidate rowname used in simplification procedure; size indicates the number of data elements in the node; components cluster id (connected component identification), infomap result of infomap algorithm.
links: a data frame with the links of the network defined by source and target. The variable size determine the number of data elements connected.
idnodes: a data frame with the id and clusters of each data element. The varaible id represents the id (rowname) of the element in the distance matrix; name identifier of the node where the data element is assigned; components cluster id, infomap result of the infomap algorithm.

Daniel Alcaide, daniel.alcaide@esat.kuleuven.be

Alcaide D, Aerts J. (2018) MCLEAN: Multilevel Clustering Exploration As Network. PeerJ Computer Science 4:e145 https://doi.org/10.7717/peerj-cs.145

data("synthetic_distances")
output = mclean(distance_matrix = synthetic_distances, threshold = 187, method = "single")
plot_network(output)

# Without node aggregation
distance_matrix = as.dist(matrix ( c( 0, 2, 3, 5, 7, 8,10, 9,11,11,
                                      2, 0, 2, 5, 6, 7, 9, 8, 9,10,
                                      3, 2, 0, 2, 6,10,13,10, 9,11,
                                      5, 5, 2, 0, 4, 6, 8, 6, 7, 9,
                                      7, 6, 6, 4, 0, 6, 7, 6, 6, 8,
                                      8, 7,10, 6, 6, 0, 3, 5, 5, 6,
                                      10, 9,13, 8, 7, 3, 0, 2, 6, 4,
                                      9, 8,10, 6, 6, 5, 2, 0, 4, 6,
                                      11, 9, 9, 7, 6, 5, 6, 4, 0, 7,
                                      11,10,11, 9, 8, 6, 4, 6, 7, 0),  byrow = TRUE, ncol = 10))

 plot_network( mclean(distance_matrix = distance_matrix, threshold = 4.1, method = "average", node_simplification = FALSE) )