Clustering from a dissimilarity matrix

TDA's clustering commands, all of which take a dissimilarity and so accept a dist – including the one tda_seqm returns.

Usage

TDA_CLUSTER

tda_cluster(
  d,
  method = "hierarchical",
  algorithm = c("centers", "diameter"),
  min_size = NULL,
  max_splits = NULL,
  options = list(),
  dir = tempfile("tda"),
  ...
)

Arguments

d: a dist, or a square dissimilarity matrix.
method: one of the names in TDA_CLUSTER, matched partially.
algorithm: for method = "hierarchical" only: split by "centers" (default, maximally different cluster centers) or "diameter" (minimal-diameter partitions) – hcld's alg=.
min_size: for algorithm = "centers", the minimum cluster size (hcld's min=).
max_splits: for algorithm = "diameter", the maximum number of splits (hcld's max=).
options: a named list of further TDA options, passed through.
dir: working directory.
...: passed to tda_run.

Value

An object carrying a table, or for "hierarchical" clusters instead (see Details). For "hierarchical" the object also carries merges: one row per split, the two clusters, their sizes and their diameters.

Details

The default, "hierarchical" (hcld), writes a ragged table – a node, its member count, then that many member indices – which is not a rectangular data.frame, so it is parsed separately into clusters, a named list of member-index vectors, one per node of the divisive tree (the root first, then each split). "unidimensional" and "additive" report their result only as text (a final partition or a fitted tree), never through df=; read $run$output for those.

Examples

d <- dist(matrix(c(1, 2, 3, 8, 9, 10), ncol = 1))
tda_cluster(d)$clusters  # the divisive tree: root, then each split
#> $`1`
#> [1] 1 2 3 4 5 6
#> 
#> $`2`
#> [1] 1 2 3
#> 
#> $`3`
#> [1] 4 5 6
#> 
#> $`4`
#> [1] 1 2
#> 
#> $`5`
#> [1] 3
#> 
#> $`6`
#> [1] 4 5
#> 
#> $`7`
#> [1] 6
#>