The p-median model as a tool for clustering psychological data.

Köhn, Hans Friedrich; Steinley, Douglas; Brusco, Michael J.

doi:10.1037/a0018535

Cited by 49 publications

(39 citation statements)

References 60 publications

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“…For example, Blanchard, Aloise, and DeSarbo (2012) developed an extension known as the heterogeneous p -median model that is particularly well-suited for applications pertaining to categorization tasks. Moreover, K -median clustering is broadly applicable to very general proximity data, including asymmetric and rectangular dissimilarity matrices (see Köhn et al, 2010). …”

Section: Resultsmentioning

confidence: 99%

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A comparison of latent class, K-means, and K-median methods for clustering dichotomous data.

Brusco

Shireman

Steinley

2017

Psychological Methods

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The problem of partitioning a collection of objects based on their measurements on a set of dichotomous variables is a well-established problem in psychological research, with applications including clinical diagnosis, educational testing, cognitive categorization, and choice analysis. Latent class analysis and K-means clustering are popular methods for partitioning objects based on dichotomous measures in the psychological literature. The K-median clustering method has recently been touted as a potentially useful tool for psychological data and might be preferable to its close neighbor, K-means, when the variable measures are dichotomous. We conducted simulation-based comparisons of the latent class, K-means, and K-median approaches for partitioning dichotomous data. Although all three methods proved capable of recovering cluster structure, K-median clustering yielded the best average performance, followed closely by latent class analysis. We also report results for the three methods within the context of an application to transitive reasoning data, where it was found that the three approaches can exhibit profound differences when applied to real data.

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Section: Resultsmentioning

confidence: 99%

“…By contrast, K -median software is often not commercially available and, instead, must be obtained from other sources. For example, Kaufman and Rousseeuw (2005) offer an R implementation of a K -median heuristic, and Köhn et al (2010) have described a suite of Matlab programs for K -median clustering.…”

Section: Resultsmentioning

confidence: 99%

A comparison of latent class, K-means, and K-median methods for clustering dichotomous data.

Brusco

Shireman

Steinley

2017

Psychological Methods

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“…To select an optimal number of clusters, we performed visual analysis of a dendrogram representing the structure of the data and confirmed that we could identify natural groupings using bivariate matrices which provided construct validation. Next, we used the k-medians cluster methodology with a Euclidean distance similarity measure (L2) to verify cluster classifications, for a set number of k clusters ranging from 3–8 (Brusco & Köhn, 2009; Kohn et al, 2010). We used the Calinski pseudo-F statistic, which measures the ratio of between cluster variance to within cluster variance as a quantitative measure of the distinctness of the groups generated by the cluster analysis and provide a stopping rule to optimize the number of groups selected (Calinski & Harabasz, 1974).…”

Section: Methodsmentioning

confidence: 99%

Residential patterns in older homeless adults: Results of a cluster analysis

Lee

Guzman

Ponath

et al. 2016

Social Science & Medicine

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Adults aged 50 and older make up half of individuals experiencing homelessness and have high rates of morbidity and mortality. They may have different life trajectories and reside in different environments than do younger homeless adults. Although the environmental risks associated with homelessness are substantial, the environments in which older homeless individuals live have not been well characterized. We classified living environments and identified associated factors in a sample of older homeless adults. From July 2013 to June 2014, we recruited a community-based sample of 350 homeless men and women aged fifty and older in Oakland, California. We administered structured interviews including assessments of health, history of homelessness, social support, and life course. Participants used a recall procedure to describe where they stayed in the prior six months. We performed cluster analysis to classify residential venues and used multinomial logistic regression to identify individual factors prior to the onset of homelessness as well as the duration of unstable housing associated with living in them. We generated four residential groups describing those who were unsheltered (n=162), cohabited unstably with friends and family (n=57), resided in multiple institutional settings (shelters, jails, transitional housing) (n=88), or lived primarily in rental housing (recently homeless) (n=43). Compared to those who were unsheltered, having social support when last stably housed was significantly associated with cohabiting and institution use. Cohabiters and renters were significantly more likely to be women and have experienced a shorter duration of homelessness. Cohabiters were significantly more likely than unsheltered participants to have experienced abuse prior to losing stable housing. Pre-homeless social support appears to protect against street homelessness while low levels of social support may increase the risk for becoming homeless immediately after losing rental housing. Our findings may enable targeted interventions for those with different manifestations of homelessness.

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“…It can be applied to cluster metric data as well as to more general similarity/dissimilarity data, even asymmetric or rectangular data structures (i.e., when not every object can be a median) (Köhn et al, 2010). Mladenović et al (2007) present an extensive review of exact and heuristic solution methods for this problem.…”

Section: Introductionmentioning

confidence: 99%