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<h5>Description:</h5>
<p>This is a python implementation of <a href="https://ieeexplore.ieee.org/abstract/document/9096616" target="_blank" rel="noopener">STAD</a> for exploration and visualization of high-dimensional data. STAD generates an abstract representation of high-dimensional data by giving each data point a location in a graph while preserving the distances in the original high-dimensional space. The package can be used to construct and interactively visualize the resulting networks. In addition, several common lens / filter functions are implemented.</p>

<h5>More Information:</h5>
<ul>
<li><strong>Grand Challenge/Workpackage(s)</strong>: <a href="/en/research/research-challenges-2019-2023/ai-driven-data-science"[entry:airesearch:58:/research/research-challenges/ai-driven-data-science]data-entry-id="58" target="_blank" rel="noopener">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:Jelmer.bot@uhasselt.be" target="_blank" rel="noopener">Jelmer Bot</a></li>
<li><strong>Programming Language</strong>: Python</li>
<li><a href="https://gitlab.com/dsi_uhasselt/vda-lab/pystad2" target="_blank" rel="noopener">Link to the repository</a></li>
<li><strong>Categories</strong>: Networks and graphs</li>
</ul>

<h5>Publications:</h5>
<ul>
<li>D. Alcaide and J. Aerts, "<a href="https://ieeexplore.ieee.org/abstract/document/9096616" target="_blank" rel="noopener">Spanning Trees as Approximation of Data Structures</a>," in IEEE Transactions on Visualization and Computer Graphics, doi: 10.1109/TVCG.2020.2995465.</li>
<li>D. Alcaide, J.&nbsp; Aerts, "<a href="https://doi.org/10.7717/peerj-cs.430" target="_blank" rel="noopener">A visual analytic approach for the identification of ICU patient subpopulations using ICD diagnostic codes</a>", PeerJ Computer Science 7:e430, 2020 .</li>
</ul>

<p>A Library for data analysis using STAD (Simplified Topological Abstraction of Data) networks.</p>