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   Articles with "number clusters" as a keyword



The effect of number of clusters and magnitude of within‐cluster homogeneity in outcomes on the performance of four variance estimators for a marginal multivariable Cox regression model fit to clustered data in the context of observational research

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recommendations! Published in 2024 at "Statistics in Medicine"

DOI: 10.1002/sim.10126

Abstract: Researchers often estimate the association between the hazard of a time‐to‐event outcome and the characteristics of individuals and the clusters in which individuals are nested. Lin and Wei's robust variance estimator is often used with… read more here.

Keywords: variance; within cluster; estimator; number clusters ... See more keywords

A Comparison of Variance Estimators for Logistic Regression Models Estimated Using Generalized Estimating Equations (GEE) in the Context of Observational Health Services Research

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recommendations! Published in 2024 at "Statistics in Medicine"

DOI: 10.1002/sim.10260

Abstract: In observational health services research, researchers often use clustered data to estimate the independent association between individual outcomes and several cluster‐level covariates after adjusting for individual‐level characteristics. Generalized estimating equations are a popular method for… read more here.

Keywords: cluster level; number clusters; variance; level ... See more keywords

Estimating the Optimal Number of Clusters Via Internal Validity Index

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recommendations! Published in 2021 at "Neural Processing Letters"

DOI: 10.1007/s11063-021-10427-8

Abstract: Estimating the optimal number of clusters (NC) is pivotal in cluster analysis. From the viewpoint of sample geometry, a novel internal clustering validity index, which is termed the between-within cluster (BWC) index, is designed in… read more here.

Keywords: data sets; number clusters; index; optimal number ... See more keywords

Automatically finding the number of clusters based on simulated annealing

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recommendations! Published in 2017 at "Journal of Shanghai Jiaotong University (Science)"

DOI: 10.1007/s12204-017-1813-9

Abstract: Based on simulated annealing (SA), automatically finding the number of clusters (AFNC) is proposed in this paper to determine the number of clusters and their initial centers. It is a simple and automatic method that… read more here.

Keywords: number clusters; mountain; based simulated; automatically finding ... See more keywords
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Estimating the Optimal Number of Clusters k in a Dataset Using Data Depth

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recommendations! Published in 2019 at "Data Science and Engineering"

DOI: 10.1007/s41019-019-0091-y

Abstract: This paper proposes a new method called depth difference (DeD), for estimating the optimal number of clusters (k) in a dataset based on data depth. The DeD method estimates the k parameter before actual clustering… read more here.

Keywords: optimal number; depth; number clusters; estimating optimal ... See more keywords

A data-driven selection of the number of clusters in the Dirichlet allocation model via Bayesian mixture modelling

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recommendations! Published in 2019 at "Journal of Statistical Computation and Simulation"

DOI: 10.1080/00949655.2019.1643345

Abstract: ABSTRACT In this paper, we consider a Bayesian mixture model that allows us to integrate out the weights of the mixture in order to obtain a procedure in which the number of clusters is an… read more here.

Keywords: number clusters; bayesian mixture; data driven; model ... See more keywords

Cluster randomized trials with a small number of clusters: which analyses should be used?

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recommendations! Published in 2018 at "International journal of epidemiology"

DOI: 10.1093/ije/dyy057

Abstract: Background: Cluster randomised trials (CRTs) are increasingly used to assess the effectiveness of health interventions. Three main analysis approaches are: cluster-level analyses, mixed-models and generalized estimating equations (GEEs). Mixed models and GEEs can lead to… read more here.

Keywords: number clusters; cluster level; small number; cluster ... See more keywords

Nonnegative Matrix Factorization Based Consensus for Clusterings With a Variable Number of Clusters

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recommendations! Published in 2018 at "IEEE Access"

DOI: 10.1109/access.2018.2874038

Abstract: Consensus clustering is an aggregation of base clusterings into an ensemble clustering which is better than the individual base clusterings. It is beneficial to determine the clusters from heterogeneous data. This paper presents a new… read more here.

Keywords: number clusters; base clusterings; consensus; base ... See more keywords

A Fully-Unsupervised Possibilistic C-Means Clustering Algorithm

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recommendations! Published in 2018 at "IEEE Access"

DOI: 10.1109/access.2018.2884956

Abstract: In 1993, Krishnapuram and Keller first proposed possibilistic C-means (PCM) clustering by relaxing the constraint in fuzzy C-means of which memberships for a data point across classes sum to 1. The PCM algorithm tends to… read more here.

Keywords: number clusters; algorithm; possibilistic means; pcm algorithm ... See more keywords

Automatic Fuzzy Clustering Using Non-Dominated Sorting Particle Swarm Optimization Algorithm for Categorical Data

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recommendations! Published in 2019 at "IEEE Access"

DOI: 10.1109/access.2019.2927593

Abstract: Categorical data clustering has been attracted a lot of attention recently due to its necessary in the real-world applications. Many clustering methods have been proposed for categorical data. However, most of the existing algorithms require… read more here.

Keywords: fuzzy clustering; automatic fuzzy; categorical data; number clusters ... See more keywords

A Centroid Auto-Fused Hierarchical Fuzzy c-Means Clustering

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recommendations! Published in 2021 at "IEEE Transactions on Fuzzy Systems"

DOI: 10.1109/tfuzz.2020.2991306

Abstract: Like k-means and Gaussian mixture model (GMM), fuzzy c-means (FCM) with soft partition has also become a popular clustering algorithm and still is extensively studied. However, these algorithms and their variants still suffer from some… read more here.

Keywords: number clusters; number; fuzzy means; caf hfcm ... See more keywords