kselect: Methods for selecting clusters
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kselect

R Documentation

Methods for selecting clusters

Description

These functions are generally not user-facing but used internally in e.g. the ⁠*_equivalence()⁠ functions. They are documented here.

Usage

k_strict(hc, .data)

k_elbow(hc, .data, census, range)

k_silhouette(hc, .data, range)

Arguments

`hc`	A hierarchical clustering object.
`.data`	An object of a `{manynet}`-consistent class: matrix (adjacency or incidence) from `{base}` R edgelist, a data frame from `{base}` R or tibble from `{tibble}` igraph, from the `{igraph}` package network, from the `{network}` package tbl_graph, from the `{tidygraph}` package
`census`	A motif census object.
`range`	An integer indicating the maximum number of options to consider. The minimum of this and the number of nodes in the network is used.

Functions

k_strict(): Selects a number of clusters in which there is no distance between cluster members.
k_elbow(): Selects a number of clusters in which there is a fair trade-off between parsimony and fit according to the elbow method.
k_silhouette(): Selects a number of clusters that optimises the silhouette score.

References

Thorndike, Robert L. 1953. "Who Belongs in the Family?". Psychometrika, 18(4): 267–76. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/BF02289263")}.

Rousseeuw, Peter J. 1987. “Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis.” Journal of Computational and Applied Mathematics, 20: 53–65. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/0377-0427(87)90125-7")}.

migraph documentation built on Nov. 2, 2023, 5:47 p.m.