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#clustering

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JMLR<p>'Curvature-based Clustering on Graphs', by Yu Tian, Zachary Lubberts, Melanie Weber.</p><p><a href="http://jmlr.org/papers/v26/24-0781.html" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">http://</span><span class="ellipsis">jmlr.org/papers/v26/24-0781.ht</span><span class="invisible">ml</span></a> <br> <br><a href="https://sigmoid.social/tags/clustering" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>clustering</span></a> <a href="https://sigmoid.social/tags/communities" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>communities</span></a> <a href="https://sigmoid.social/tags/clusters" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>clusters</span></a></p>
Fabrice Tshimanga<p>Exciting news, our paper is out!</p><p>"Behavioral Clusters and Lesion Distributions in Ischemic Stroke, Based on NIHSS Similarity Network" on Springer Journal of Healthcare Informatics Research <a href="https://rdcu.be/efgma" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">rdcu.be/efgma</span><span class="invisible"></span></a></p><p>With my co-first-author Andrea Zanola and co-authors, we explore the relations between behavioral measures of impairment after stroke, and the underlying brain lesions.<br>Rather than focusing on covariances at the population level, we first cluster individual behavioral phenotypes, and then explore the typical and significant lesions of each cluster.</p><p>Our technique, Repeated Spectral Clustering is performed on a similarity network (derived from the General Distance Measure, handy for ordinal scales!), and the partitions are statistically robust thanks to the aggregation of results from multiple random initializations.</p><p>We end up with 5 clusters, 3 of which show reknown principal components of deficits (Left Motor, Righ Motor, Language), and their associate lesions.</p><p>Interestingly, this multi-item and multimodal approach allows to distinguish different etiologies for the same deficits, thanks to their different behavioral associations, and the different lesions characterizing each cluster. Even when the single NIHSS measure is a bit "vague"...</p><p>We hope that popularizing the General Distance Measure, Repeated Spectral Clustering and this clustering perspective aside of PCA / CCA studies can inspire multimodal approaches in other neuroscientific and biomedical domains!</p><p>Many thanks to our co-authors, Antonio Luigi Bisogno, Silvia Facchini, Lorenzo Pini, Manfredo Atzori and Maurizio Corbetta for data, analytic and medical insights, and their guidance throughout the whole process!</p><p><a href="https://neuromatch.social/tags/stroke" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>stroke</span></a> <a href="https://neuromatch.social/tags/neuroscience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>neuroscience</span></a> <a href="https://neuromatch.social/tags/clustering" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>clustering</span></a> <a href="https://neuromatch.social/tags/machinelearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>machinelearning</span></a></p>
Teresita Porter 🙋🏻‍♀️<p>**OptimOTU: Taxonomically aware OTU clustering with optimized thresholds and a bioinformatics workflow for metabarcoding data**</p><p><a href="https://arxiv.org/abs/2502.10350" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">arxiv.org/abs/2502.10350</span><span class="invisible"></span></a></p><p><a href="https://ecoevo.social/tags/OTU" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>OTU</span></a> <a href="https://ecoevo.social/tags/clustering" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>clustering</span></a> <a href="https://ecoevo.social/tags/bioinformatics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bioinformatics</span></a> <a href="https://ecoevo.social/tags/DNAmetabarcoding" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DNAmetabarcoding</span></a></p>
IB Teguh TM<p>Explore Linkage Criteria in Hierarchical Clustering. Understand Single, Complete, Average Linkage, and Ward's Method to enhance your clustering skills. <a href="https://mastodon.social/tags/DataScience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DataScience</span></a> <a href="https://mastodon.social/tags/Clustering" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Clustering</span></a> <a href="https://mastodon.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>MachineLearning</span></a></p><p><a href="https://teguhteja.id/linkage-criteria-in-hierarchical-clustering-a-tutorial/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">teguhteja.id/linkage-criteria-</span><span class="invisible">in-hierarchical-clustering-a-tutorial/</span></a></p>
Leslie García<p>¿Como trabajan con sus archivos de grabación de audio? si son archivos enormes a mi me gusta un poco de ayuda, normalmente analizo con algún algoritmo de trasients, beats, onsets para poder hacer cortes mas precisos, luego con un algoritmo de clustering eliminar esos segmentos de audio que se parecen demasiado, y organizarlos por similitudes. Hice una versión con GUI de esa herramienta para compartirla.</p><p><a href="https://mastodon.social/tags/sound" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>sound</span></a> <a href="https://mastodon.social/tags/clustering" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>clustering</span></a> <a href="https://mastodon.social/tags/python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>python</span></a> <a href="https://mastodon.social/tags/tools" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>tools</span></a></p>
JuliaR<p>👋 Hi all <a href="https://fosstodon.org/tags/Rstats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Rstats</span></a> enthusiasts!<br>I'm looking for someone who has time now to conduct a review of a piece of software for Journal of Open Source Software (JOSS). Details are here:<br><a href="https://github.com/openjournals/joss-reviews/issues/7319" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">github.com/openjournals/joss-r</span><span class="invisible">eviews/issues/7319</span></a></p><p>The review process is quite simple - you get a checklist and you run some tests. It's all open, on GitHub.</p><p><a href="https://fosstodon.org/tags/PeerReview" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PeerReview</span></a> <a href="https://fosstodon.org/tags/softwaredevelopment" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>softwaredevelopment</span></a> <a href="https://fosstodon.org/tags/OpenSource" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>OpenSource</span></a> <a href="https://fosstodon.org/tags/programming" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>programming</span></a> <a href="https://fosstodon.org/tags/clustering" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>clustering</span></a> <a href="https://fosstodon.org/tags/DataScience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DataScience</span></a> <a href="https://fosstodon.org/tags/Bioinformatics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Bioinformatics</span></a></p>
noisediver<p>Biologists, stop putting UMAP plots in your papers</p><p><a href="https://med-mastodon.com/tags/UMAP" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>UMAP</span></a> is a powerful tool for exploratory data analysis, but without a clear understanding of how it works, it can easily lead to confusion and misinterpretation.</p><p><a href="https://simplystatistics.org/posts/2024-12-23-biologists-stop-including-umap-plots-in-your-papers/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">simplystatistics.org/posts/202</span><span class="invisible">4-12-23-biologists-stop-including-umap-plots-in-your-papers/</span></a></p><p><a href="https://med-mastodon.com/tags/clustering" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>clustering</span></a> <a href="https://med-mastodon.com/tags/dimensionalityreduction" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>dimensionalityreduction</span></a> <a href="https://med-mastodon.com/tags/dataviz" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>dataviz</span></a></p>
Europe Says<p><a href="https://www.europesays.com/1760442/" rel="nofollow noopener noreferrer" target="_blank"><span class="invisible">https://www.</span><span class="">europesays.com/1760442/</span><span class="invisible"></span></a> Statistical and data visualization techniques to study the role of one-electron in the energy of neutral and charged clusters of Na39 <a href="https://pubeurope.com/tags/Clustering" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Clustering</span></a> <a href="https://pubeurope.com/tags/Data" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Data</span></a> <a href="https://pubeurope.com/tags/DataVisualization" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DataVisualization</span></a> <a href="https://pubeurope.com/tags/DensityFunctionalTheory" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DensityFunctionalTheory</span></a> <a href="https://pubeurope.com/tags/Energy" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Energy</span></a> <a href="https://pubeurope.com/tags/HumanitiesAndSocialSciences" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>HumanitiesAndSocialSciences</span></a> <a href="https://pubeurope.com/tags/multidisciplinary" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>multidisciplinary</span></a> <a href="https://pubeurope.com/tags/Regression" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Regression</span></a> <a href="https://pubeurope.com/tags/science" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>science</span></a> <a href="https://pubeurope.com/tags/SodiumCluster" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>SodiumCluster</span></a> <a href="https://pubeurope.com/tags/StatisticalAnalysis" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>StatisticalAnalysis</span></a> <a href="https://pubeurope.com/tags/StatisticalPhysics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>StatisticalPhysics</span></a> <a href="https://pubeurope.com/tags/statistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statistics</span></a> <a href="https://pubeurope.com/tags/TimeSeries" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>TimeSeries</span></a></p>
Towards Data Science<p>Shared Nearest Neighbors (SNN) — A distance metric that can improve prediction, clustering, and outlier detection in datasets with many dimensions and with varying densities. Read more from W Brett Kennedy now!</p><p><a href="https://me.dm/tags/Clustering" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Clustering</span></a> <a href="https://me.dm/tags/AnomalyDetection" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>AnomalyDetection</span></a> </p><p><a href="https://towardsdatascience.com/shared-nearest-neighbors-a-more-robust-distance-metric-064d7f99ffb7" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">towardsdatascience.com/shared-</span><span class="invisible">nearest-neighbors-a-more-robust-distance-metric-064d7f99ffb7</span></a></p>
Barry Schwartz<p>How clustering works with localization in Google Search <a href="https://www.seroundtable.com/google-search-clustering-localization-38531.html" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">seroundtable.com/google-search</span><span class="invisible">-clustering-localization-38531.html</span></a></p><p><a href="https://c.im/tags/google" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>google</span></a> <a href="https://c.im/tags/seo" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>seo</span></a> <a href="https://c.im/tags/localizations" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>localizations</span></a> <a href="https://c.im/tags/clustering" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>clustering</span></a> <a href="https://c.im/tags/canonicalization" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>canonicalization</span></a></p>
Barry Schwartz<p>Google on the difference between clustering and canonicalization: "Clustering is basically taking the pages that we think are the same. And then canonicalization is, from those pages, which one is the best one" <span class="h-card" translate="no"><a href="https://mastodon.social/@johnmu" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>johnmu</span></a></span> said <a href="https://www.seroundtable.com/google-search-clustering-canonicalization-38529.html" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">seroundtable.com/google-search</span><span class="invisible">-clustering-canonicalization-38529.html</span></a></p><p><a href="https://c.im/tags/seo" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>seo</span></a> <a href="https://c.im/tags/google" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>google</span></a> <a href="https://c.im/tags/canonicalization" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>canonicalization</span></a> <a href="https://c.im/tags/clustering" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>clustering</span></a> <a href="https://c.im/tags/search" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>search</span></a></p>
Towards Data Science<p>Exploring the power of Graph Based Clustering! This technique transforms point cloud data into structured, analyzable clusters by leveraging KD-Trees and connected component analysis. Read Florent Poux, Ph.D.'s latest article now.</p><p>@PouxPointCloud</p><p><a href="https://me.dm/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>MachineLearning</span></a> <a href="https://me.dm/tags/Clustering" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Clustering</span></a></p><p><a href="https://towardsdatascience.com/3d-clustering-with-graph-theory-the-complete-guide-38b21b1c8748" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">towardsdatascience.com/3d-clus</span><span class="invisible">tering-with-graph-theory-the-complete-guide-38b21b1c8748</span></a></p>
HGPU group<p>Thesis: CLUEstering: a high-performance density-based clustering library for scientific computing</p><p><a href="https://mast.hpc.social/tags/HIP" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>HIP</span></a> <a href="https://mast.hpc.social/tags/CUDA" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CUDA</span></a> <a href="https://mast.hpc.social/tags/Clustering" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Clustering</span></a> <a href="https://mast.hpc.social/tags/Astrophysics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Astrophysics</span></a> <a href="https://mast.hpc.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>MachineLearning</span></a> <a href="https://mast.hpc.social/tags/ML" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ML</span></a></p><p><a href="https://hgpu.org/?p=29571" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">hgpu.org/?p=29571</span><span class="invisible"></span></a></p>
Daniel Hoffmann 🥬<p>Well-connectedness of communities: Park et al show that many communities detected with standard algorithms are not well-connected: by cutting a few edges, such a community breaks into 2. Remedy by post-processing.</p><p><a href="https://doi.org/10.1371/journal.pcsy.0000009" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">doi.org/10.1371/journal.pcsy.0</span><span class="invisible">000009</span></a></p><p><a href="https://mathstodon.xyz/tags/clustering" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>clustering</span></a><br> <a href="https://mathstodon.xyz/tags/communitydetection" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>communitydetection</span></a></p>