2014
DOI: 10.1186/1471-2288-14-113
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A comparison of three clustering methods for finding subgroups in MRI, SMS or clinical data: SPSS TwoStep Cluster analysis, Latent Gold and SNOB

Abstract: BackgroundThere are various methodological approaches to identifying clinically important subgroups and one method is to identify clusters of characteristics that differentiate people in cross-sectional and/or longitudinal data using Cluster Analysis (CA) or Latent Class Analysis (LCA). There is a scarcity of head-to-head comparisons that can inform the choice of which clustering method might be suitable for particular clinical datasets and research questions. Therefore, the aim of this study was to perform a … Show more

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Cited by 163 publications
(126 citation statements)
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“…LCA seeks to identify membership in latent (i.e., unobserved) groups/classes comprised of individuals characterized by a specific profile with regard to a given set of observed variables. There are several advantages in using LCA over traditional algorithm-based clustering techniques (e.g., K-means clustering, cluster analysis using distance methods) including the use of a probabilistic statistical model to determine classes that reduces the uncertainty in class classification (Kent, Jensen, & Kongsted, 2014; Muthén, 2001; Vermunt, & Magidson, 2002). In particular, this probabilistic model-based approach allows for the use of appropriate fit statistics (maxim likelihood statistics) to assess the fit of model, and thus, reduces the uncertainty of classification (Vermunt, & Magidson, 2002).…”
Section: Methodsmentioning
confidence: 99%
“…LCA seeks to identify membership in latent (i.e., unobserved) groups/classes comprised of individuals characterized by a specific profile with regard to a given set of observed variables. There are several advantages in using LCA over traditional algorithm-based clustering techniques (e.g., K-means clustering, cluster analysis using distance methods) including the use of a probabilistic statistical model to determine classes that reduces the uncertainty in class classification (Kent, Jensen, & Kongsted, 2014; Muthén, 2001; Vermunt, & Magidson, 2002). In particular, this probabilistic model-based approach allows for the use of appropriate fit statistics (maxim likelihood statistics) to assess the fit of model, and thus, reduces the uncertainty of classification (Vermunt, & Magidson, 2002).…”
Section: Methodsmentioning
confidence: 99%
“…30 Both studies used distance-based clustering procedures, rather than the probabilistic method used in this study, which may afford greater model validity. 36,49 Assessment of power of LCA to identify true latent classes is in development, and it is likely that the sample size in this study may be inadequate. Although recommendations for minimum samples (200-300) exist, power may be influenced by model-specific factors, including the size and number of true latent classes and model complexity.…”
Section: ) "mentioning
confidence: 99%
“…The empirical results showed that clusters obtained by two‐step clustering are high quality rules when compared to other discretization methods. Kent et al () concluded that two‐step clustering, Latent Gold clustering and SNOB clustering obtained a near‐perfect detection of known subgroups and correctly classified individuals into those subgroups.…”
Section: Proposed Technique: Data Mining Based Outlier Detectionmentioning
confidence: 99%
“…1 Tkaczynski (2017) implies that two-step clustering has been performed in many researches since its first version. It has been used in many applications from tourism to health, marketing to transportation (Ahlqvist et al, 2017;Kent, Jensen, & Kongsted, 2014;Cerin, Leslie, Du Toit, Owen, & Frank, 2007;Grifin et al, 2014;Hsu, Kang, & LAM, 2006). Beside its user friendly usage it has also many scientific advantages too.…”
Section: Outlier Detection With Two-step Clusteringmentioning
confidence: 99%
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