> For the complete documentation index, see [llms.txt](https://time-series-features.gitbook.io/hctsa-manual/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://time-series-features.gitbook.io/hctsa-manual/installing-and-using-hctsa/analyzing_visualizing/clustering_rows_and_columns.md).

# Clustering rows and columns

For the purposes of visualizing the data matrix, it is often desirable to have the rows and columns reordered to put similar rows adjacent to one another, and similarly to place similar columns adjacent to one another. This reordering can be done using hierarchical linkage clustering, by the function `TS_Cluster`:

```
distanceMetricRow = 'euclidean'; % time-series feature distance
linkageMethodRow = 'average'; % linkage method
distanceMetricCol = 'corr_fast'; % a (poor) approximation of correlations with NaNs
linkageMethodCol = 'average'; % linkage method

TS_Cluster(distanceMetricRow, linkageMethodRow, distanceMetricCol, linkageMethodCol);
```

This function reads in the data from `HCTSA_N.mat`, and stores the re-ordering of rows and columns back into `HCTSA_N.mat` in the `ts_clust` and `op_clust` structures (and, if the size is manageable, also the pairwise distance information). By default, pairwise distances are stored for time series (or operations) only if there are at most 1000 of them; the optional fifth input, `doSave`, sets this explicitly (for time series and operations, as a two-element vector), and the sixth input specifies a custom file to cluster. Specifying `'none'` as a distance metric skips clustering of the rows or columns.

Visualization functions use this information automatically: [`TS_PlotDataMatrix`](/hctsa-manual/installing-and-using-hctsa/analyzing_visualizing/visualizing_the_data_matrix.md) reorders rows and columns by the stored clustering whenever it exists, and [`TS_SimSearch`](/hctsa-manual/installing-and-using-hctsa/analyzing_visualizing/sim_search.md) uses the stored pairwise distances to find nearest neighbors.


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