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#clustering

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Public
💧🌏 Greg Cocks

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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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

photo - looking down into Medellin, Aburrá Valley, Columbia from a surrounding high ground
annotated maps with imagery - Areas of analysis of the results by the SMA1 methodological route and kurtosis. (A). Central Park in Bello (B). Parques del Río Medellín (C). Arkadia Shopping center; (D). Peldar Plant (E). La García water supply reservoir (F). Conasfaltos dam (G). La Ayurá stream basin in Envigado (H). Central Park in Bello (I). Avenida Regional Norte (J). Vía Distribuidora Sur.
schematic / work flow - proposed methodology for analysis of zonal land cover changes
annonated maps - The Aburrá Valley (white line) between the valleys of the Magdalena and Cauca rivers. Data were acquired from ALOS PALSAR Terrain Corrected and data from IGAC.
Public
Teresita Porter 🙋🏻‍♀️

**OptimOTU: Taxonomically aware OTU clustering with optimized thresholds and a bioinformatics workflow for metabarcoding data**

arxiv.org/abs/2502.10350

arXiv.orgOptimOTU: Taxonomically aware OTU clustering with optimized thresholds and a bioinformatics workflow for metabarcoding dataTo turn environmentally derived metabarcoding data into community matrices for ecological analysis, sequences must first be clustered into operational taxonomic units (OTUs). This task is particularly complex for data including large numbers of taxa with incomplete reference libraries. OptimOTU offers a taxonomically aware approach to OTU clustering. It uses a set of taxonomically identified reference sequences to choose optimal genetic distance thresholds for grouping each ancestor taxon into clusters which most closely match its descendant taxa. Then, query sequences are clustered according to preliminary taxonomic identifications and the optimized thresholds for their ancestor taxon. The process follows the taxonomic hierarchy, resulting in a full taxonomic classification of all the query sequences into named taxonomic groups as well as placeholder "pseudotaxa" which accommodate the sequences that could not be classified to a named taxon at the corresponding rank. The OptimOTU clustering algorithm is implemented as an R package, with computationally intensive steps implemented in C++ for speed, and incorporating open-source libraries for pairwise sequence alignment. Distances may also be calculated externally, and may be read from a UNIX pipe, allowing clustering of large datasets where the full distance matrix would be inconveniently large to store in memory. The OptimOTU bioinformatics pipeline includes a full workflow for paired-end Illumina sequencing data that incorporates quality filtering, denoising, artifact removal, taxonomic classification, and OTU clustering with OptimOTU. The OptimOTU pipeline is developed for use on high performance computing clusters, and scales to datasets with millions of reads per sample, and tens of thousands of samples.
#OTU#clustering#bioinformatics
Public
Leslie García

¿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.

#sound#clustering#python
Public *
Gilgwath

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: medium.com/@camphul/cloudnativ using #longhorn and #cloundnativepg and some smart sheduling. #k8s #selfhosting

Medium · CloudNative-PG in the homelab with Longhorn - Luca Camphuisen - MediumBy Luca Camphuisen
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💧🌏 Greg Cocks

Stanford Researchers Map ‘White-Only’ Properties In Santa Clara Co. Using AI [ historic deeds / covenants ]
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kron4.com/news/bay-area/stanfo <-- shared media article
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dho.stanford.edu/wp-content/up <-- shared research
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reglab.github.io/racialcovenan <-- link to shared webmap
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#GIS #spatial #mapping #California #deeds #property #racial #racism #redlining #covenenants #race #minorities #propertyrecords #discrimination #history #historical #USHistory #legalreform #records #AI #machinelearning #openlargelanguagemodel #model #modeling #geography #clustering #demographics #spatialanalysis #spatiotemporal

snapshot - 1913 housing advertisement for the Palm Haven neighborhood in San Jose. 1913 is several years before Buchanan found “restricted districts” based on race unconstitutional, and the advertisement emphasizes the “restricted district[].” Palm Haven construction dates straddled Buchanan. It was developed by Thomas Herschbach who came to be responsible for 161 racial covenants in the County
snapshot - portion of deed - Although racially restrictive covenants are no longer legally enforceable and are considered illegal under the Fair Housing Act today, they still exist in thousands, possibly even millions, of historical property records in California. One such example, found in a 1940 real property deed from Santa Clara County’s archives, contains the following discriminatory language: “No persons not of the Caucasian Race shall be allowed to occupy, except as servants of residents, said real property or any part thereof.” The deed further specifies that “[t]hese covenants are to run with the land and shall be binding on all parties,” thereby affecting not only the tenants at the time but also the potential future owners of the land.
Charts – Top: Number of property deeds with restrictive covenants from 1905–1974, divided by whether specific racial groups were excluded or only white/Caucasian individuals were permitted. Most pre-1915 covenants specifically exclude Black and Asian individuals, but the vast majority of later covenants are whiteonly. The small number of restrictive covenants matched after 1970 consists largely of older deeds filed for reference, rather than new restrictive covenants being introduced. Bottom: The number of occurrences of specific racial groups in covenants that exclude specific groups. East Asian and Black were by far the most commonly excluded demographics, but some covenants targeted other groups, such as Italian, Portuguese, Indian, and Mexican individuals.
Maps – Top: Clusters of racial covenants on a map of modern-day Santa Clara County. Some of the largest and most notable racially restricted developments – discussed in this section – are shown in red. Bottom left: Racial covenants in south Palo Alto and Mountain View. Bottom right: Racial covenants in downtown San Jose. Dots represent individual subdivisions and are scaled in proportion to the number of racial covenants within the subdivision.
Public
Bioinfo-fr.net

🗞️ Nous accueillons aujourd'hui un nouvel auteur : Évoluscope 🥳 Il s'attaque à un gros chapitre en nous expliquant les différences entre les algorithmes de clustering :
bioinfo-fr.net/la-diversite-de
#bioinfofr #clustering #algorithme #Bioinformatics

Bioinfo-fr.net · La diversité des algorithmes de clustering - Bioinfo-fr.netIntroduction Cette plante est-elle comestible ? Cet animal est-il dangereux ? Classer des choses similaires dans une même catégorie et des choses dissimilaires dans différentes catégories est une idée intuitive que les enfants pratiquent dès le plus jeune âge. On utilise ainsi le même mot, « une chaise » par exemple, pour désigner une très grande variété d’objets possédant […]
Continued thread
Public
Harald Klinke

Two great sources to explore the use of pan and zoom techniques in data visualization:

1. Shneiderman's "information-seeking mantra" emphasizes the importance of overview, zoom, and filter in exploring data clusters.
infovis-wiki.net/wiki/Visual_I
2. "Zoomland" (de Gruyter, 2023), edited by Armaselu and Fickers, offers insights on zooming in data visualization.
degruyter.com/document/doi/10.

infovis-wiki.netVisual Information-Seeking Mantra - InfoVis:Wiki
#DataViz#KenBurnsEffect#Clustering
Public
Antonio Lieto

The paper "Interpretable Clusters for Representing Citizens’ Sense of Belonging through Interaction with Cultural Heritage" has been published in the ACM Journal of Computing and Cultural Heritage.

📝 Title: Interpretable Clusters for Representing Citizens’ Sense of Belonging through Interaction with Cultural Heritage

🌐 Index Terms: technology for #culturalheritage, #clustering
#affectivecomputing; social cohesion; #museum interaction
Full paper: doi.org/10.1145/3665142
@academicchatter

Continued thread
Public
Tim Kellogg

I’ve also gone deep into #clustering algorithms. I’m coming to the conclusion that K-Means has assumptions that don’t work well for me, and probably usually don’t work. Some big ones:

- clusters are the same size
- the number of clusters is known

I’m clustering posts by embedding (text content/meaning). Most of the time I don’t know how many posts there are, and my feed is too dynamic for these assumptions to hold.

I’m learning about other algorithms, like DBSCAN

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