Stability analysis in <i>K</i>‐means clustering

Steinley, Douglas

doi:10.1348/000711007x184849

Cited by 35 publications

(30 citation statements)

References 44 publications

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“…This approximation of the stability matrix was already proposed by several authors under different names (Ben-Hur et al, 2002;Fred and Jain, 2005;Steinley, 2008;Fred and Lourenço, 2008), even though the relationship with the probabilistic formulation in Eq. 2 was not made explicitly.…”

Section: Bootstrap Analysis Of Stable Clusters (Basc)mentioning

confidence: 82%

Multi-level bootstrap analysis of stable clusters in resting-state fMRI

et al. 2010

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Section: Bootstrap Analysis Of Stable Clusters (Basc)mentioning

confidence: 82%

Multi-level bootstrap analysis of stable clusters in resting-state fMRI

et al. 2010

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“…Consensus clustering is a popular approach within the statistics and machine learning literatures, (e.g., Monti et al, 2003; Steinley, 2008; Strehl and Ghosh, 2002), and has been successfully applied to iFC data in several previous studies (Bellec et al, 2010; Kelly et al, 2010; van den Heuvel et al, 2008). The aim of consensus clustering is to improve the robustness of clustering results to sampling variability.…”

Section: Methodsmentioning

confidence: 99%

A convergent functional architecture of the insula emerges across imaging modalities

et al. 2012

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Empirical evidence increasingly supports the hypothesis that patterns of intrinsic functional connectivity (iFC) are sculpted by a history of evoked coactivation within distinct neuronal networks. This, together with evidence of strong correspondence among the networks defined by iFC and those delineated using a variety of other neuroimaging techniques, suggests a fundamental brain architecture detectable across multiple functional and structural imaging modalities. Here, we leverage this insight to examine the functional organization of the human insula. We parcellated the insula on the basis of three distinct neuroimaging modalities – task-evoked coactivation, intrinsic (i.e., task-independent) functional connectivity, and grey matter structural covariance. Clustering of these three different covariance-based measures revealed a convergent elemental organization of the insula that likely reflects a fundamental brain architecture governing both brain structure and function at multiple spatial scales. While not constrained to be hierarchical, our parcellation revealed a pseudo-hierarchical, multiscale organization that was consistent with previous clustering and meta-analytic studies of the insula. Finally, meta-analytic examination of the cognitive and behavioral domains associated with each of the insular clusters obtained elucidated the broad functional dissociations likely underlying the topography observed. To facilitate future investigations of insula function across healthy and pathological states, the insular parcels obtained here have been made freely available for download via http://fcon_1000.projects.nitrc.org, along with the analytic scripts used to perform the parcellations.

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“…Specifically, Steinley (2006, 2007, 2008) and Steinley and Brusco (2011) generated distributions based on random data to test the quality of a cluster solution and to determine whether the correct number of clusters had been chosen, respectively. Using this general approach, Steinley (2004) developed a method for sampling cluster agreement-matrices to approximate the statistic’s sampling distribution 3 .…”

Section: Background For Proposed Testmentioning

confidence: 99%

A method for making inferences in network analysis: Comment on Forbes, Wright, Markon, and Krueger (2017).

Steinley¹,

Hoffman²,

Brusco³

et al. 2017

Journal of Abnormal Psychology

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Forbes, Wright, Markon, and Krueger (2017) make a compelling case for proceeding cautiously with respect to the overinterpretation and dissemination of results using the increasingly popular approach of creating “networks” from co-occurrences of psychopathology symptoms. We commend the authors on their initial investigation and their utilization of cross-validation techniques in an effort to capture the stability of a variety of network estimation methods. Such techniques get at the heart of establishing “reproducibility”, an increasing focus of concern in both psychology (e.g., Pashler & Wegenmakers, 2012) and science more generally (e.g., Baker, 2016). However, as we will show, the problem is likely worse (or at least more complicated) than they initially indicated. Specifically, for multivariate binary data, the marginal distributions enforce a large degree of structure on the data. We show that some expected measurements – such as commonly used centrality statistics – can have substantially higher values than what would usually be expected. As such, we propose a nonparametric approach to generate confidence intervals through Monte Carlo simulation. We apply the proposed methodology to the National Comorbidity Survey - Replication, provided by Forbes et al, finding that the many of the results are indistinguishable from what would be expected by chance. Further, we discuss the problem of multiple testing and potential issues of applying methods developed for one-mode networks (e.g., ties within a single set of observations) to two-mode networks (e.g., ties between two distinct sets of entities). When taken together, these issues indicate that the psychometric network models should be employed with extreme caution and interpreted guardedly.

show abstract

Stability analysis in K‐means clustering

Cited by 35 publications

References 44 publications

Multi-level bootstrap analysis of stable clusters in resting-state fMRI

Multi-level bootstrap analysis of stable clusters in resting-state fMRI

A convergent functional architecture of the insula emerges across imaging modalities

A method for making inferences in network analysis: Comment on Forbes, Wright, Markon, and Krueger (2017).

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