find_k | R Documentation |

There are a wide range of algorithms and visual techniques to identify a number of clusters or principal components embeded in the observed data.

```
find_k()
```

It is critical to explore the eigenvalues, cluster stability, and visualization.
See R packages `bootcluster`

, `EMCluster`

, and `nFactors`

.

Please see the R package `SC3`

, which provides `estkTW()`

function to
find the number of significant eigenvalues according to the Tracy-Widom test.

`ADPclust`

package includes `adpclust()`

function that runs the algorithm
on a range of K values. It helps you to identify the most suitable number of clusters.

This package also provides an alternative methods in `permutationPA`

.
Through a resampling-based Parallel Analysis, it finds a number of significant components.

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