Exciting news, our paper is out!

"Behavioral Clusters and Lesion Distributions in Ischemic Stroke, Based on NIHSS Similarity Network" on Springer Journal of Healthcare Informatics Research https://rdcu.be/efgma

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.
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.

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.

We end up with 5 clusters, 3 of which show reknown principal components of deficits (Left Motor, Righ Motor, Language), and their associate lesions.

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"...

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!

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!

'Bayesian Sparse Gaussian Mixture Model for Clustering in High Dimensions', by Dapeng Yao, Fangzheng Xie, Yanxun Xu.

http://jmlr.org/papers/v26/23-0142.html

#sparse #clustering #clusters

A Methodology For The Multitemporal Analysis Of Land Cover Changes And Urban Expansion Using Synthetic Aperture Radar (SAR) Imagery - A Case Study Of The Aburrá Valley In Colombia
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https://doi.org/10.3390/rs17030554 <-- shared paper
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#GIS #spatial #mapping #SyntheticApertureRadar #SAR #remotesensing #multitemporalanalysis #landcover #landcoverchange #clustering #kurtosis #fuzzylogic #kernelbasedmethod #machinelearning #spatialanalysis #spatiotemporal #geostatistics #model #modeling #AburráValley #Columbia #urban #urbanexpansion #population #growth #topography #monitoring #satellite #sentinel #valley #landuse #distribution #infrastructure #building #roads #naturalresources #environmental #conservation #monitoring #multitemporal

'An Axiomatic Definition of Hierarchical Clustering', by Ery Arias-Castro, Elizabeth Coda.

http://jmlr.org/papers/v26/24-1052.html

#cluster #clustering #hierarchical

Explore Linkage Criteria in Hierarchical Clustering. Understand Single, Complete, Average Linkage, and Ward's Method to enhance your clustering skills. #DataScience #Clustering #MachineLearning

https://teguhteja.id/linkage-criteria-in-hierarchical-clustering-a-tutorial/

Master K-means Clustering with Rand Index and Adjusted Rand Score. Unlock valuable insights into your data by grouping and evaluating points for maximum clarity. #kmeans #clustering

https://teguhteja.id/k-means-clustering-master-rand-index-adjusted-rand-score/

Two new publications in Computo in January 2025!

The first one, by Félix Laplante and Christophe Ambroise, introduces a new clustering algorithm called Spectral Bridges.

This algorithm detects high-density areas by merging (through spectral clustering) the cells of of a Voronoi tesselation (obtained through k-means) that exhibit a strong bridge affinity.

Bridge affinity is a geometric criterion introduced by the authors, which corresponds to a notion of margin between clusters.

The paper then benchmarks Spectral Bridges against state-of-the art hierarchical clustering, density-based clustering, and centroid-based clustering methods, on data of varying size and dimensions, and demonstrate its superiority in a variety of settings.

The paper is available here: https://doi.org/10.57750/1gr8-bk61

For once, the paper does not directly include code, as code is provided in two libraries, one in Python: https://pypi.org/project/spectral-bridges/ and the other in R: https://github.com/cambroise/spectral-bridges-Rpackage

'Robust Spectral Clustering with Rank Statistics', by Joshua Cape, Xianshi Yu, Jonquil Z. Liao.

http://jmlr.org/papers/v25/23-0552.html

#clustering #spectral #cluster

¿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.

Hi all #Rstats enthusiasts!
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:
https://github.com/openjournals/joss-reviews/issues/7319

The review process is quite simple - you get a checklist and you run some tests. It's all open, on GitHub.

When you are reading up on deploying #databases the most frequent piece of drive-by advice is "don't use networked storage". Before you can ask the smart ass what they suggest instead in an age of #virtualization #clustering and #kubernetes they have already disappeared into the ether. Not an easy nut to crack, especially in a #homelab. This guy has an actual workable answer: https://medium.com/@camphul/cloudnative-pg-in-the-homelab-with-longhorn-b08c40b85384 using #longhorn and #cloundnativepg and some smart sheduling. #k8s #selfhosting

https://www.europesays.com/1760442/ Statistical and data visualization techniques to study the role of one-electron in the energy of neutral and charged clusters of Na39 #Clustering #Data #DataVisualization #DensityFunctionalTheory #Energy #HumanitiesAndSocialSciences #multidisciplinary #Regression #science #SodiumCluster #StatisticalAnalysis #StatisticalPhysics #statistics #TimeSeries

'Distributed Kernel-Driven Data Clustering', by Ioannis Schizas.

http://jmlr.org/papers/v25/23-0669.html

#clustering #distributed #semidefinite

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!

#Clustering #AnomalyDetection

https://towardsdatascience.com/shared-nearest-neighbors-a-more-robust-distance-metric-064d7f99ffb7

How clustering works with localization in Google Search https://www.seroundtable.com/google-search-clustering-localization-38531.html

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" @johnmu said https://www.seroundtable.com/google-search-clustering-canonicalization-38529.html

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.

@PouxPointCloud

#MachineLearning #Clustering

https://towardsdatascience.com/3d-clustering-with-graph-theory-the-complete-guide-38b21b1c8748

'Graphical Dirichlet Process for Clustering Non-Exchangeable Grouped Data', by Arhit Chakrabarti et al.

http://jmlr.org/papers/v25/23-1048.html

#clustering #clusters #hypergraph