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#clustering

1 post1 participant0 posts today
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Miki :rstats:

#30DayChartChallenge Día 14: Kinship! 🌿 Hoy toca visualizar "parentescos" animales, pero basados en ¡similitud de rasgos! #RelationshipsWeek #Animals

Este dendrograma horizontal es el resultado de un clustering jerárquico (hclust Ward.D2) sobre ~170 especies, usando su Masa Corporal y Longevidad Máxima (log-transformadas y escaladas). ¡Muestra quién se agrupa con quién según su estrategia de vida!

Las ramas unen las especies más similares. La longitud horizontal hasta la unión indica cuán diferentes son. Se ven grandes grupos que separan, por ejemplo, animales muy grandes/longevos de otros más pequeños/rápidos. Es una forma de ver la estructura oculta en los datos de rasgos.

(Solo se muestra 1/3 de las etiquetas para no saturar!)

🛠 #rstats #ggplot2 #ggdendro #stats | Datos: Kaggle (S. Banerjee)
📂 Código/Viz: t.ly/Y_fwt

Dendrograma horizontal que visualiza el clustering jerárquico de aproximadamente 170 especies animales, basado en la similitud de su masa corporal y longevidad máxima (log-transformadas y escaladas). El árbol se ramifica de izquierda a derecha. Los nombres de las especies (un subconjunto) aparecen como etiquetas en las puntas de las ramas a la derecha. El eje vertical representa las especies/clusters, y el eje horizontal (superior, etiquetado como 'Altura') indica la distancia o disimilitud a la que se unen los clusters. El gráfico utiliza un tema con fondo beige claro y líneas/texto en marrón. Título: "Similitud Animal Basada en Masa y Longevidad". Fuente: Kaggle dataset by S. Banerjee.
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#Day14#Kinship#dataviz
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💧🌏 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
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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.
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schematic / work flow - proposed methodology for analysis of zonal land cover changes
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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.
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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
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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.
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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.
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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.
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Replied to Fabrice Tshimanga
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Fabrice Tshimanga

4/n

Reverting our General Distance matrix into the General Similarity matrix yields an ambiguous spectrum, whose eigenvalues do not help to determine the number of clusters in the data.
But repeating clustering and tracing which subjects consistently get clustered together, actually yields the right information, encoded in a co-occurrence matrix.
This latter is quite evidently composed of 5 main clusters.
Our second approach, affinity propagation, found autonomously 7 clusters, that are mainly finer grained partitions of the former 5.

#machinelearning#clustering
Replied to Fabrice Tshimanga
Public
Fabrice Tshimanga

3/n

We thus decided to use the General Distance Measure to compute pairwise similarities between our 172 subjects, and obtained a matrix, which as math savy people know, is also the description of a network (an "adjacency matrix" for a "weighted undirected graph").
The problem was then to find cliques, communities or clusters of similar patients in such a network, and we used spectral clustering.
Spectral clustering is a family of techniques that use spectra of matrices describing networks, i.e. use eigenvalues of matrices to understand the structure of those networks.

#spectralanalysis#spectralclustering#clustering
Public
Fabrice Tshimanga

1/n
Our pre-print is finally out!
Here's my first #paperthread 🧵
In this work, co-authors and I clustered ischaemic stroke patients profiles, and recovered common patterns of cognitive, sensorimotor damage.

...Historically many focal lesions to specific cortical areas were associated with specific distinction, but most strokes involve subcortical regions and bring multivariate patterns of deficits.
To characterize those patterns, many studies have turned to correlation analysis, factor analysis, PCA, focusing on the relations among variables==domains of impairments...

medrxiv.org/content/10.1101/20

medRxiv · Behavior Clusters in Ischemic Stroke using NIHSSBACKGROUND Stroke is one of the leading causes of death and disability. The resulting behavioral deficits can be measured with clinical scales of motor, sensory, and cognitive impairment. The most common of such scales is the National Institutes of Health Stroke Scale, or NIHSS. Computerized tomography (CT) and magnetic resonance imaging (MRI) scans show predominantly subcortical or subcortical-cortical lesions, with pure cortical lesions occurring less frequently. While many experimental studies have correlated specific deficits (e.g. motor or language impairment) with stroke lesion locations, the mapping between symptoms and lesions is not straightforward in clinical practice. The advancement of machine learning and data science in recent years has shown unprecedented opportunities even in the biomedical domain. Nevertheless, their application to medicine is not simple, and the development of data driven methods to learn general mathematical models of diseases from healthcare data is still an unsolved challenge. METHODS In this paper we measure statistical similarities of stroke patients based on their NIHSS scores, and we aggregate symptoms profiles through two different unsupervised machine learning techniques: spectral clustering and affinity propagation. RESULTS We identify clusters of patients with largely overlapping, coherent lesions, based on the similarity of behavioral profiles. CONCLUSIONS Overall, we show that an unsupervised learning workflow, open source and transferable to other conditions, can identify coherent mathematical representations of stroke lesions based only on NIHSS data. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported by the Department of excellence 2018-2022 initiative of the Italian Ministry of education (MIUR) awarded to the Department of Neuroscience-University of Padua. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: For data of patients of the Saint Louis cohort: the Internal Review Board of Washington University School of Medicine (WUSM) gave ethical approval for this work. For data of patients of the Padua cohort: the Ethics Committee of the Azienda Ospedale Universit&agrave Padova (AOUP) gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data can be made available upon reasonable request to Maurizio Corbettta at maurizio.corbetta{at}unipd.it. * AP : Affinity Propagation. GDM : General Distance Measure. GSM : General Similarity Measure. NIHSS : National Institutes of Health Stroke Scale. RSC : Repeated Spectral Clustering.
#stroke#neuroscience#machinelearning
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Evan Light

Federated link aggregators should not own individual topics. Topics should belong to the entire fedi.

Instance owned topics is just another form of centralization.

We should not only discourage topic diaspora (e.g., a "gaming" topic on beehaw is separate from a gaming topic on another instance) but engineer solutions to *allow* and default unification of content by topic across the fedi for link aggregation (but not force this).

(De)federation then becomes how we keep topics safe for our communities: blocking posts and comments from demonstrably unsafe instances.

This is a CAP distributed system problem. My instincts say that federated link aggregation is a different enough problem from federated blogging (mastodon) that we need federated instances to share content via a consensus algorithm and not the mastodon style of federation.

I wonder about the practicalities of making this work in the truly heterogenous system of the #fediverse.

Perhaps by having the federation members/instances themselves share capability and capacity metadata with the cluster so as to load balance intelligently? The alternative could be an unintentional DDOS due to a massive instance going offline, shedding traffic to instances too small to handle the load.

#linkaggregation#cap#distributedsystems
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dialecticDolt

Ahhhh #Introduction
I'm a phd student in #hpc and #computationalscience.
Making computers go brrr for large scale data analytics (#clustering, nearest neighbors, #imageprocessing, #linearalgebra) & dabble in tools for task based #parallelism. Hope to see some of ya'll @ #SC22!

I listen to more #ska than is acceptable in 2022, and will lie about having better music tastes than the digimon movie soundtrack. @ me with loose leaf #tea and #book recs (always reading #fiction & #webserials)

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