clusteringKmeans {seqsetvis} | R Documentation

## perform kmeans clustering on matrix rows and return reordered matrix along with order matched cluster assignments. clusters are sorted using hclust on centers

### Description

perform kmeans clustering on matrix rows and return reordered matrix along with order matched cluster assignments. clusters are sorted using hclust on centers

### Usage

    clusteringKmeans(mat, nclust, centroids = NULL, iter.max = 30)

### Arguments

`mat` | numeric matrix to cluster.
`nclust` | the number of clusters.
`centroids` | optional matrix with same columns as mat and one centroid per row to base clusters off of. Overrides any setting to nclust. Default of NULL results in randomly initialized k-means.
`iter.max` | Number of max iterations to allow for k-means. Default is 30.

### Value

data.table with group__ variable indicating cluster membership and id__ variable that is a factor indicating order based on within cluster similarity

### Examples

    dt = data.table::copy(CTCF_in_10a_profiles_dt)
    mat = data.table::dcast(dt, id ~ sample + x, value.var = "y" )
    rn = mat$id
    mat = as.matrix(mat[,-1])
    rownames(mat) = rn
    clust_dt = clusteringKmeans(mat, nclust = 3)
    dt = merge(dt, clust_dt[, .(id = id__, group = group__)])
    dt$id = factor(dt$id, levels = clust_dt$id)
    dt[order(id)]

[Package seqsetvis version 1.14.4 Index]