Results 121 to 130 of about 18,151,622 (163)

Discovery of generalizable TBI phenotypes using multivariate time-series clustering. [PDF]

Comput Biol Med
Ghaderi H, Foreman B, Reddy CK, Subbian V.   +3 more
europepmc   +1 more source

Time-series clustering of the migration speed of nucleolus-precursor bodies in human zygotes and live birth following assisted reproductive technology. [PDF]

J Assist Reprod Genet
Inoue T, Taguchi S, Uemura M, Yamashita Y.   +3 more
europepmc   +1 more source

Mathematical modelling and time series clustering of Mpox outbreak: A comparative study of the top 10 affected countries and implications for future outbreak management. [PDF]

Glob Epidemiol
Tamakloe MD, Kuuwill A, Osumanu I, Siripi H.   +3 more
europepmc   +1 more source

Time series clustering and characterization

Among the vast volumes of data generated daily in our modern digital world, time series data represents a major category with broad applicability. Time series data refers to a collection of data points indexed by time, and it has major practical uses in ...
Lie, Rhys
core  

Corrigendum to 'Mathematical modelling and time series clustering of Mpox outbreak: A comparative study of the top 10 affected countries and implications for future outbreak management' [Global Epidemiology 10 (2025) 100214]. [PDF]

Glob Epidemiol
Tamakloe MD, Kuuwill A, Osumanu I, Siripi H.   +3 more
europepmc   +1 more source

A dynamic customer segmentation approach by combining LRFMS and multivariate time series clustering. [PDF]

Sci Rep
Wang S, Sun L, Yu Y.
europepmc   +1 more source

Nanomechanical mass measurements through feature-based time series clustering. [PDF]

Rev Sci Instrum
Neumann AP, Gomez A, Nunn AR, Sader JE, Roukes ML.   +4 more
europepmc   +1 more source

Investigating Older Adults' Use of a Socially Assistive Robot via Time Series Clustering and User Profiling: Descriptive Analysis Study. [PDF]

JMIR Form Res
Yoo IJ, Park DH, Lee OE, Park A.
europepmc   +1 more source

Predicting CKD progression using time-series clustering and light gradient boosting machines. [PDF]

Sci Rep
Saito H, Yoshimura H, Tanaka K, Kimura H, Watanabe K, Tsubokura M, Ejiri H, Zhao T, Ozaki A, Kazama S, Shimabukuro M, Asahi K, Watanabe T, Kazama JJ.   +13 more
europepmc   +1 more source

Somtimes: self organizing maps for time series clustering and its application to serious illness conversations. [PDF]

Data Min Knowl Discov
Javed A, Rizzo DM, Lee BS, Gramling R.
europepmc   +1 more source