Results 241 to 250 of about 2,020,230 (285)

Polarization‐resolved femtosecond Vis/IR spectroscopy tailored for resolving weak signals in biological samples using minimal sample volume

FEBS Open Bio, EarlyView.
Unique biological samples, such as site‐specific mutant proteins, are available only in limited quantities. Here, we present a polarization‐resolved transient infrared spectroscopy setup with referencing to improve signal‐to‐noise tailored towards tracing small signals. We provide an overview of characterizing the excitation conditions for polarization‐
Clark Zahn, Karsten Heyne
wiley   +1 more source

Reducing annotation effort in agricultural data: simple and fast unsupervised coreset selection with DINOv2 and K-means. [PDF]

Front Plant Sci
Gómez-Zamanillo L, Portilla N, Picón A, Egusquiza I, Navarra-Mestre R, Elola A, Bereciartua-Perez A.   +6 more
europepmc   +1 more source

Research on collaborative filtering algorithm based on improved K-means algorithm for user attribute rating and co-rating. [PDF]

Sci Rep
Zhang S, Chen S, Yu X, Mei S.
europepmc   +1 more source

Prediabetes risk classification algorithm via carotid bodies and K-means clustering technique. [PDF]

PeerJ Comput Sci
Pinheiro RF, Guarino MP, Lages M, Fonseca-Pinto R.   +3 more
europepmc   +1 more source

Predicting Group-Based Trajectories of Oral Health-Related Quality of Life From Late Adolescence to Early Adulthood Using K-Means Clustering Algorithm. [PDF]

J Public Health Dent
Ogwo C, Brown G, Warren J, Zeng E, Thomson M, Levy S.   +5 more
europepmc   +1 more source

Identifying inflammatory phenotypes associated with lung involvement in systemic sclerosis: k-means clustering approach. [PDF]

Front Immunol
Cano-García L, García-Studer A, Manrique-Arija S, Ortiz-Marquez F, Redondo-Rodríguez R, Borregón-Garrido P, Mena-Vázquez N, Fernández-Nebro A.   +7 more
europepmc   +1 more source

Assessment of intratumor heterogeneity in non-small cell lung cancer by unsupervised K-means clustering of radiomics features based on multiphase computed tomography images. [PDF]

Quant Imaging Med Surg
Wang R, Wang L, Feng H, Jing Y, Wang X, Fang J, Du J, E L.   +7 more
europepmc   +1 more source

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Subspace K-means clustering

Behavior Research Methods, 2013
To achieve an insightful clustering of multivariate data, we propose subspace K-means. Its central idea is to model the centroids and cluster residuals in reduced spaces, which allows for dealing with a wide range of cluster types and yields rich interpretations of the clusters. We review the existing related clustering methods, including deterministic,
Timmerman, Marieke E., Ceulemans, Eva, De Roover, Kim, Van Leeuwen, Karla   +3 more
openaire   +2 more sources

PERFORMANCE COMPARISON OF K-MEANS, PARALLEL K-MEANS AND K-MEANS++

K-means clustering is a fundamental unsupervised machine learning technique widely applied in various domains such as data analysis, pattern recognition, and clustering-based tasks. However, its efficiency and scalability can be challenged, particularly when dealing with large-scale datasets and complex data structures.
Aliguliyev, Ramiz, Shalala F. Tahirzada
openaire   +1 more source

Generalized Reduced K–Means

Computational Statistics
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bottazzi Schenone, Mariaelena, Rocci, Roberto, Vichi, Maurizio   +2 more
openaire   +2 more sources