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Evolutionary Monte Carlo Methods for Clustering.
Nevertheless, existing methods for clustering XML documents are designed to work in a centralized way.
Experiments on artificial and real data sets show that HDDC outperforms existing methods for clustering high-dimensional data.
Accordingly, we use Expectation Maximization (EM), Principal Component Analysis (PCA) and Adaptive Neuro-Fuzzy Inference System (ANFIS) methods for clustering, noise removal and prediction tasks, respectively.
He and his colleagues have applied and evaluated novel quantitative methods for clustering patients into longitudinal and dynamic adherence trajectories, shown their relationship to long-term clinical outcomes, and demonstrated the capacity to predict a patient's membership in each of these adherence trajectories with great accuracy.
A variety of methods for clustering photographs have been proposed [12, 13].
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Kaplan-Meier methods for clustered data estimated symptom-free survival time.
His research interests include the design of CRTs and analysis methods for clustered data, among many other interests.
For the adjusted analysis we will perform regression analysis of individual level data using methods for clustered data, multilevel, hierarchical or random effects models [ 37, 43].
Since the unit of randomisation is the PHCC, we will use a regression analysis of individual level data using methods for clustered data [ 38].
An EWCDS is an effective method for clustering.
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