Highly comparative time-series analysis with hctsa
  • Information about hctsa
    • Introduction
    • Getting started
    • Publications using hctsa
    • UMAP Projections
    • Related Time-Series Resources
    • List of included code files
    • FAQ
  • Installing and using hctsa
    • General advice and common pitfalls
    • Installing and setting up
      • Structure of the hctsa framework
      • Overview of an hctsa analysis
      • Compiling binaries
    • Running hctsa computations
      • Input files
      • Performing calculations
      • Inspecting errors
      • Working with hctsa files
    • Analyzing and visualizing results
      • Assigning group labels to data
      • Filtering and normalizing
      • Clustering rows and columns
      • Visualizing the data matrix
      • Plotting the time series
      • Low dimensional representation
      • Finding nearest neighbors
      • Investigating specific operations
      • Exploring classification accuracy
      • Finding informative features
      • Interpreting features
      • Comparing to existing features
      • Working with short time series
    • Working with a mySQL database
      • Setting up the mySQL database
      • The database structure
      • Populating the database with time series and operations
      • Adding time series
      • Retrieving from the database
      • Computing operations and writing back to the database
      • Cycling through computations using runscripts
      • Clearing or removing data
      • Retrieving data from the database
      • Error handling and maintenance
Powered by GitBook
On this page

Was this helpful?

Export as PDF
  1. Installing and using hctsa
  2. Analyzing and visualizing results

Clustering rows and columns

PreviousFiltering and normalizingNextVisualizing the data matrix

Last updated 5 years ago

Was this helpful?

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 (and, if the size is manageable, also the pairwise distance information). Visualization functions (such as and ) can then take advantage of this information, using the general input label 'cl'.

TS_PlotDataMatrix
TS_SimSearch