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<h5>Description:</h5>
<p>STAD is a dimensionality reduction algorithm, that generates an abstract representation of high-dimensional data by giving each data point a location in a graph which preserves the distances in the original high-dimensional space. The STAD graph is built upon the Minimum Spanning Tree (MST) to which new edges are added until the correlation between the graph and the original dataset is maximized. Additionally, STAD supports the inclusion of filter functions to analyse data from new perspectives, emphasizing traits in data which otherwise would remain hidden.</p>

<h5>More Information:</h5>
<ul>
<li><strong>Grand Challenge/Workpackage(s)</strong>: <a href="/en/onderzoek/onderzoeksuitdagingen/ai-driven-data-science" data-entry-id="58" target="_blank" rel="noopener" data-entry-id="58">GC1</a> - WP3</li>
<li><strong>Responsible Research Lead</strong>: <a href="mailto:jan.aerts@uhasselt.be" target="_blank" rel="noopener">Jan Aerts</a></li>
<li><strong>Authors</strong>: <a href="mailto:jan.aerts@uhasselt.be" target="_blank" rel="noopener">Jan Aerts</a>, D. Alcaide</li>
<li><strong>Programming Language</strong>: Python</li>
<li><a href="https://gitlab.com/vda-lab/pystad2" target="_blank" rel="noopener">Link to the Repository</a></li>
<li><strong>Categories</strong>: Optimization</li>
</ul>

<h5>Publications:</h5>
<ul>
<li>
<p>Alcaide, D., &amp; Aerts, J. (2020). Spanning Trees as Approximation of Data Structures. IEEE Transactions on Visualization and Computer Graphics. https://doi.org/10.1109/TVCG.2020.2995465</p>
<p>&nbsp;</p>
</li>
</ul>

<p>Visualisation technique to display topological information in high-dimensional data</p>