Computo<p>Summer ☀️ read: a new paper on model-based clustering just appeared in Computo!</p><p>Julien Jacques and Brendan Thomas Murphy publish a new method for clustering multivariate count data. The method combines feature selection and clustering, and is based on conditionally independent Poisson mixture models and Poisson generalized linear models.</p><p>On simulations, the Adjusted Rand Index (ARI) of the model with selected variables is close to the optimal ARI obtained with the true clustering variables.</p><p>The paper and accompanying R code are available at <a href="https://computo-journal.org/published-202507-jacques-count-data/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">computo-journal.org/published-</span><span class="invisible">202507-jacques-count-data/</span></a></p><p><a href="https://mathstodon.xyz/tags/machineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>machineLearning</span></a> <a href="https://mathstodon.xyz/tags/clustering" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>clustering</span></a> <a href="https://mathstodon.xyz/tags/Rstats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Rstats</span></a> <a href="https://mathstodon.xyz/tags/openScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>openScience</span></a> <a href="https://mathstodon.xyz/tags/openSource" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>openSource</span></a> <a href="https://mathstodon.xyz/tags/openAccess" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>openAccess</span></a></p>