Profiling local optima in K-means clustering: developing a diagnostic technique

Astrocyte-derived dominance winning reverses chronic stress-induced depressive behaviors

Individuals with low social status are at heightened risk of major depressive disorder (MDD), and MDD also influences social status. While the interrelationship between MDD and social status is well-defined, the behavioral causality between these two phenotypes remains unexplored. Here, we investigated the behavioral relationships between depressive and dominance behaviors in male mice exposed to chronic restraint stress and the role of medial prefrontal cortex (mPFC) astrocytes in these behaviors. Chronic restraint stress induced both depressive and submissive behaviors. Chemogenetic mPFC astrocyte activation significantly enhanced dominance in chronic stress-induced submissive mice by increasing the persistence of defensive behavior, although it did not affect depressive behaviors. Notably, repetitive winning experiences following mPFC astrocyte stimulation exerted anti-depressive effects in chronic restraint stress-induced depressive mice. These data indicate that mPFC astrocyte-derived winning experience renders anti-depressive effects, and may offer a new strategy for treating depression caused by low status in social hierarchies by targeting mPFC astrocytes.

Estimating parasite-condition relationships and potential health effects for fallow deer (Dama dama) and red deer (Cervus elaphus) in Denmark

Parasites can exert a substantial influence on the ecology of wildlife populations by altering host condition. Our objectives were to estimate single and multiparasite-condition relationships for fallow deer (Dama dama) and red deer (Cervus elaphus) in Denmark and to assess potential health effects along the parasite burden gradient. Fallow deer hosted on average two endoparasite taxa per individual (min = 0, max = 5) while red deer carried on average five parasite taxa per individual (min = 2, max = 9). Body condition of both deer species was negatively related to presence of Trichuris ssp. eggs while body condition of red deer was positively related to antibodies of the protozoan Toxoplasma gondii. For the remaining parasite taxa (n = 12), we either found weak or no apparent association between infection and deer body condition or low prevalence levels restricted formal testing. Importantly, we detected a strong negative relationship between body condition and the sum of endoparasite taxa carried by individual hosts, a pattern that was evident in both deer species. We did not detect systemic inflammatory reactions, yet serology revealed reduced total protein and iron concentrations with increased parasite load in both deer species, likely due to maldigestion of forage or malabsorption of nutrients. Despite moderate sample sizes, our study highlights the importance of considering multiparasitism when assessing body condition impacts in deer populations. Moreover, we show how serum chemistry assays are a valuable diagnostic tool to detect subtle and sub-clinical health impacts of parasitism, even at low-level infestation.

Sirtuin 4 activates autophagy and inhibits tumorigenesis by upregulating the p53 signaling pathway

The role of autophagy in cancer is context-dependent. In the present study, we aimed to investigate the regulator and underlying mechanism of autophagy. We found that a sirtuin (SIRT) family member, SIRT4, was significantly associated autophagy pathway in pancreatic ductal adenocarcinoma (PDAC). Specifically, in vitro cell culture experiments and in vivo transgenic and xenografted animal models revealed that SIRT4 could inhibit tumor growth and promote autophagy in PDAC. In terms of the mechanism, we demonstrated that SIRT4 activated the phosphorylation of p53 protein by suppressing glutamine metabolism, which was crucial in SIRT4-induced autophagy. AMPKα was implicated in the regulation of autophagy and phosphorylation of p53 mediated by SIRT4, contributing to the suppression of pancreatic tumorigenesis. Notably, the clinical significance of the SIRT4/AMPKα/p53/autophagy axis was demonstrated in human PDAC specimens. Collectively, these findings suggested that SIRT4-induced autophagy further inhibited tumorigenesis and progression of PDAC, highlighting the potential of SIRT4 as a therapeutic target for cancer.

Influence of segmentation approaches on the before-after evaluation of engineering treatments: A hypothetical treatment approach

The segmentation of highways is a fundamental step in estimating crash frequency models and conducting a before-after evaluation of engineering treatments, but the effects of segmentation approaches on the engineering treatment evaluations are not known very well. This study examined the effects of segmentation approaches on the before-after evaluation of engineering treatments. In particular, this study evaluated four segmentation approaches by applying the Empirical Bayes technique to a dataset for which the ground truth was known. Four segmentation approaches included Highway Safety Manual (HSM), Fixed (kilometre post), Fisher's, and K-means segmentation. This study utilized a 440 km stretch of rural two-lane two-way highway in Queensland, Australia, to prepare a dataset with known ground truth. The treatment under evaluation was a hypothetical treatment, which should yield a crash modification factor (CMF) of 1. For assigning hypothetical treatment, a total of fifteen datasets were prepared, including ten datasets based on the random assignment and five datasets based on the hotspot identification method. Following the before-after evaluation using the Empirical Bayes technique, the results showed that HSM and Fixed segmentation approaches predict the ground truth in both dataset types. From random assignment datasets, the estimated CMFs using HSM, Fixed, Fisher's, and K-means segmentation approaches deviated from the true CMF (i.e., 1) by 2.32 %, 5.30 %, 6.08 %, and 8.62 %, respectively. In the case of hotspots, the corresponding deviations of CMFs were 8.57 %, 9.37 %, 28.84 %, and 35.43 %, respectively. Overall, HSM segmentation best identified the actual treatment effect, followed by the Fixed segmentation. If the variables to define homogeneity for HSM segmentation are limited, then Fixed segmentation can yield reliable crash modification factors from the before-after treatment evaluations than the crash-based segmentation approaches.

Benchmarking distance-based partitioning methods for mixed-type data

Clustering mixed-type data, that is, observation by variable data that consist of both continuous and categorical variables poses novel challenges. Foremost among these challenges is the choice of the most appropriate clustering method for the data. This paper presents a benchmarking study comparing eight distance-based partitioning methods for mixed-type data in terms of cluster recovery performance. A series of simulations carried out by a full factorial design are presented that examined the effect of a variety of factors on cluster recovery. The amount of cluster overlap, the percentage of categorical variables in the data set, the number of clusters and the number of observations had the largest effects on cluster recovery and in most of the tested scenarios. KAMILA, K-Prototypes and sequential Factor Analysis and K-Means clustering typically performed better than other methods. The study can be a useful reference for practitioners in the choice of the most appropriate method.

Band depth based initialization of K-means for functional data clustering

The k-Means algorithm is one of the most popular choices for clustering data but is well-known to be sensitive to the initialization process. There is a substantial number of methods that aim at finding optimal initial seeds for k-Means, though none of them is universally valid. This paper presents an extension to longitudinal data of one of such methods, the BRIk algorithm, that relies on clustering a set of centroids derived from bootstrap replicates of the data and on the use of the versatile Modified Band Depth. In our approach we improve the BRIk method by adding a step where we fit appropriate B-splines to our observations and a resampling process that allows computational feasibility and handling issues such as noise or missing data. We have derived two techniques for providing suitable initial seeds, each of them stressing respectively the multivariate or the functional nature of the data. Our results with simulated and real data sets indicate that our Functional Data Approach to the BRIK method (FABRIk) and our Functional Data Extension of the BRIK method (FDEBRIk) are more effective than previous proposals at providing seeds to initialize k-Means in terms of clustering recovery.

A Triple-Network Dynamic Connection Study in Alzheimer's Disease

Alzheimer's disease (AD) was associated with abnormal organization and function of large-scale brain networks. We applied group independent component analysis (Group ICA) to construct the triple-network consisting of the saliency network (SN), the central executive network (CEN), and the default mode network (DMN) in 25 AD, 60 mild cognitive impairment (MCI) and 60 cognitively normal (CN) subjects. To explore the dynamic functional network connectivity (dFNC), we investigated dynamic time-varying triple-network interactions in subjects using Group ICA analysis based on k-means clustering (GDA-k-means). The mean of brain state-specific network interaction indices (meanNII) in the three groups (AD, MCI, CN) showed significant differences by ANOVA analysis. To verify the robustness of the findings, a support vector machine (SVM) was taken meanNII, gender and age as features to classify. This method obtained accuracy values of 95, 94, and 77% when classifying AD vs. CN, AD vs. MCI, and MCI vs. CN, respectively. In our work, the findings demonstrated that the dynamic characteristics of functional interactions of the triple-networks contributed to studying the underlying pathophysiology of AD. It provided strong evidence for dysregulation of brain dynamics of AD.

Instructor facilitation mediates students' negative perceptions of active learning instruction

Studies have demonstrated students' resistance to active learning, despite evidence illustrating that their learning is improved relative to students in lectures. Specifically, while active learning and group work are effective at engaging students in their learning process, studies report that students' perceptions of active learning approaches are not always positive. What remains underexplored is whether students' perceptions of active learning improve with effective instructor facilitation and whether there exists differential perceptions between racially minoritized students and represented students. Here, we estimate students' perceptions of effective instructor facilitation as the mediator in the relationship between active learning and perceptions of learning and perceived utility for class activities (task value). Then, we examine differences by racial identification. We collected classroom observation data to empirically categorize courses as active learning or lecture-based and surveyed 4,257 college students across 25 STEM classrooms at a research-intensive university. We first examined the relationship between active learning on student perceptions and found a negative relationship between active learning and perceptions of learning and task value for both racially minoritized students and represented students. Next, we assessed whether students' perceptions of instructor effectiveness in facilitating group activities mediate these negative relationships. We found that, on average, students of all races were more likely to positively perceive instructor facilitation in active learning classes relative to lectures. In turn, the positive perceptions of instructor facilitation partially suppressed the negative relationship between active learning and perceptions of learning and task value. These results demonstrate that effective instructor facilitation can influence both students' self-assessment of learning and perceived utility of the learning activities, and underscores the importance of developing pedagogical competence among college instructors.

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

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 & Wagenmakers, 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 1-mode networks (e.g., ties within a single set of observations) to 2-mode networks (e.g., ties between 2 distinct sets of entities). When taken together, these issues indicate that the psychometric network models should be employed with extreme caution and interpreted guardedly. (PsycINFO Database Record

A note on the expected value of the Rand index

Two expectations of the adjusted Rand index (ARI) are compared. It is shown that the expectation derived by Morey and Agresti (1984, Educational and Psychological Measurement, 44, 33) under the multinomial distribution to approximate the exact expectation from the hypergeometric distribution (Hubert & Arabie, 1985, Journal of Classification, 2, 193) provides a poor approximation, and, in some cases, the difference between the two expectations can increase with the sample size. Proofs concerning the minimum and maximum difference between the two expectations are provided, and it is shown through simulation that the ARI can differ significantly depending on which expectation is used. Furthermore, when compared in a hypothesis testing framework, multinomial approximation overly favours the null hypothesis.

Detecting Clusters/Communities in Social Networks

Cohen's κ, a similarity measure for categorical data, has since been applied to problems in the data mining field such as cluster analysis and network link prediction. In this paper, a new application is examined: community detection in networks. A new algorithm is proposed that uses Cohen's κ as a similarity measure for each pair of nodes; subsequently, the κ values are then clustered to detect the communities. This paper defines and tests this method on a variety of simulated and real networks. The results are compared with those from eight other community detection algorithms. Results show this new algorithm is consistently among the top performers in classifying data points both on simulated and real networks. Additionally, this is one of the broadest comparative simulations for comparing community detection algorithms to date.

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

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 3 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 3 methods within the context of an application to transitive reasoning data, in which it was found that the 3 approaches can exhibit profound differences when applied to real data. (PsycINFO Database Record

Examining the effect of initialization strategies on the performance of Gaussian mixture modeling

Mixture modeling is a popular technique for identifying unobserved subpopulations (e.g., components) within a data set, with Gaussian (normal) mixture modeling being the form most widely used. Generally, the parameters of these Gaussian mixtures cannot be estimated in closed form, so estimates are typically obtained via an iterative process. The most common estimation procedure is maximum likelihood via the expectation-maximization (EM) algorithm. Like many approaches for identifying subpopulations, finite mixture modeling can suffer from locally optimal solutions, and the final parameter estimates are dependent on the initial starting values of the EM algorithm. Initial values have been shown to significantly impact the quality of the solution, and researchers have proposed several approaches for selecting the set of starting values. Five techniques for obtaining starting values that are implemented in popular software packages are compared. Their performances are assessed in terms of the following four measures: (1) the ability to find the best observed solution, (2) settling on a solution that classifies observations correctly, (3) the number of local solutions found by each technique, and (4) the speed at which the start values are obtained. On the basis of these results, a set of recommendations is provided to the user.

Cluster analysis in empirical OM research: survey and recommendations

Purpose

The purpose of this paper is twofold. First, the authors provide a survey of operations management (OM) research applications of traditional hierarchical and nonhierarchical clustering methods with respect to key decisions that are central to a valid analysis. Second, the authors offer recommendations for practice with respect to these decisions.

Design/methodology/approach

A coding study was conducted for 97 cluster analyses reported in six OM journals during the period spanning 1994-2015. Data were collected with respect to: variable selection, variable standardization, method, selection of the number of clusters, consistency/stability of the clustering solution, and profiling of the clusters based on exogenous variables. Recommended practices for validation of clustering solutions are provided within the context of this framework.

Findings

There is considerable variability across clustering applications with respect to the components of validation, as well as a mix of productive and undesirable practices. This justifies the importance of the authors’ provision of a schema for conducting a cluster analysis.

Research limitations/implications

Certain aspects of the coding study required some degree of subjectivity with respect to interpretation or classification. However, in light of the sheer magnitude of the coding study (97 articles), the authors are confident that an accurate picture of empirical OM clustering applications has been presented.

Practical implications

The paper provides a critique and synthesis of the practice of cluster analysis in OM research. The coding study provides a thorough foundation for how the key decisions of a cluster analysis have been previously handled in the literature. Both researchers and practitioners are provided with guidelines for performing a valid cluster analysis.

Originality/value

To the best of the authors’ knowledge, no study of this type has been reported in the OM literature. The authors’ recommendations for cluster validation draw from recent studies in other disciplines that are apt to be unfamiliar to many OM researchers.

Local Optima in Mixture Modeling

It is common knowledge that mixture models are prone to arrive at locally optimal solutions. Typically, researchers are directed to utilize several random initializations to ensure that the resulting solution is adequate. However, it is unknown what factors contribute to a large number of local optima and whether these coincide with the factors that reduce the accuracy of a mixture model. A real-data illustration and a series of simulations are presented that examine the effect of a variety of data structures on the propensity of local optima and the classification quality of the resulting solution. We show that there is a moderately strong relationship between a solution that has a high proportion of local optima and one that is poorly classified.

Posttraumatic stress disorder among refugees: Measurement invariance of Harvard Trauma Questionnaire scores across global regions and response patterns

Despite the central role of posttraumatic stress disorder (PTSD) in international humanitarian aid work, there has been little examination of the measurement invariance of PTSD measures across culturally defined refugee subgroups. This leaves mental health workers in disaster settings with little to support inferences made using the results of standard clinical assessment tools, such as the severity of symptoms and prevalence rates. We examined measurement invariance in scores from the most widely used PTSD measure in refugee populations, the Harvard Trauma Questionnaire (HTQ; Mollica et al., 1992), in a multinational and multilingual sample of asylum seekers from 81 countries of origin in 11 global regions. Clustering HTQ responses to justify grouping regional groups by response patterns resulted in 3 groups for testing measurement invariance: West Africans, Himalayans, and all others. Comparing log-likelihood ratios showed that while configural invariance seemed to hold, metric and scalar invariance did not. These findings call into question the common practice of using standard cut-off scores on PTSD measures across culturally dissimilar refugee populations. In addition, high correlation between factors suggests that the construct validity of scores from North American and European measures of PTSD may not hold globally.

A Note on Using the Adjusted Rand Index for Link Prediction in Networks

As network data gains popularity for research in various fields, the need for methods to predict future links or find missing links in the data increases. One subset of the methodology used to solve this problem involves creating a similarity measure between each pair of nodes in the network; unfortunately, these methods can be shown to have arbitrary cutoffs and poor performance. To address these shortcomings, we use the adjusted Rand index to create a similarity measure between nodes that has a natural threshold of zero. The effectiveness of this method is then compared to a number of other similarity measures and assessed on a variety of simulated data sets with block model structure and three real network data sets. Under this particular formulation of the adjusted Rand index, information is also provided on dissimilarity. As such, we then go on to test its use for detecting incorrect links in network data, highlighting the dual use of the approach.

Cattle grazing in semiarid forestlands: Habitat selection during periods of drought

Climate change models are predicting increased frequency and severity of droughts in arid and semiarid environments, and these areas are responsible for much of the world's livestock production. Because cattle (Bos Taurus) grazing can impact the abundance, distribution, and ecological function of native plant and animal communities, it is important to understand how cattle might respond to increasingly arid conditions. Here, we evaluate changes in habitat selection by cattle across an 8-yr period as a function of rainfall and other environmental covariates. Using resource selection functions, we evaluated habitat selection based on 2 behaviors, stationary and mobile. Models revealed similarity in cattle habitat selection across years, with only modest changes in selection as a function of precipitation, despite marked seasonal and interannual differences in rainfall. Cattle preferred gentle slopes, forest edges, wet meadows, and areas near water as well as areas far from water on plateaus. Cattle avoided areas at intermediate distances from water, typically associated with steep slopes. As conditions became drier during the late season, cattle did not switch selection patterns but instead contracted their selection around water. Cattle also selected similar habitats whether they were mobile or stationary, possibly making microsite decisions therein. This consistent pattern of selection across years could be particularly problematic for riparian communities as climates become drier; however, it may also simplify cattle management, as range managers can focus vegetation monitoring efforts on riparian areas. Due to the uncertainty surrounding future climatic conditions, it is imperative that both range and wildlife managers develop long-term plans to continue managing these multiuse landscapes in an ecologically sustainable manner based on expected patterns of livestock grazing.

Model selection for minimum-diameter partitioning

The minimum-diameter partitioning problem (MDPP) seeks to produce compact clusters, as measured by an overall goodness-of-fit measure known as the partition diameter, which represents the maximum dissimilarity between any two objects placed in the same cluster. Complete-linkage hierarchical clustering is perhaps the best-known heuristic method for the MDPP and has an extensive history of applications in psychological research. Unfortunately, this method has several inherent shortcomings that impede the model selection process, such as: (1) sensitivity to the input order of the objects, (2) failure to obtain a globally optimal minimum-diameter partition when cutting the tree at K clusters, and (3) the propensity for a large number of alternative minimum-diameter partitions for a given K. We propose that each of these problems can be addressed by applying an algorithm that finds all of the minimum-diameter partitions for different values of K. Model selection is then facilitated by considering, for each value of K, the reduction in the partition diameter, the number of alternative optima, and the partition agreement among the alternative optima. Using five examples from the empirical literature, we show the practical value of the proposed process for facilitating model selection for the MDPP.

Finding reproducible cluster partitions for the k-means algorithm

K-means clustering is widely used for exploratory data analysis. While its dependence on initialisation is well-known, it is common practice to assume that the partition with lowest sum-of-squares (SSQ) total i.e. within cluster variance, is both reproducible under repeated initialisations and also the closest that k-means can provide to true structure, when applied to synthetic data. We show that this is generally the case for small numbers of clusters, but for values of k that are still of theoretical and practical interest, similar values of SSQ can correspond to markedly different cluster partitions.

This paper extends stability measures previously presented in the context of finding optimal values of cluster number, into a component of a 2-d map of the local minima found by the k-means algorithm, from which not only can values of k be identified for further analysis but, more importantly, it is made clear whether the best SSQ is a suitable solution or whether obtaining a consistently good partition requires further application of the stability index. The proposed method is illustrated by application to five synthetic datasets replicating a real world breast cancer dataset with varying data density, and a large bioinformatics dataset.

Principal Cluster Axes: A Projection Pursuit Index for the Preservation of Cluster Structures in the Presence of Data Reduction

A measure of "clusterability" serves as the basis of a new methodology designed to preserve cluster structure in a reduced dimensional space. Similar to principal component analysis, which finds the direction of maximal variance in multivariate space, principal cluster axes find the direction of maximum clusterability in multivariate space. Furthermore, the principal clustering approach falls into the class of projection pursuit techniques. Comparisons are made with existing methodologies both in a simulation study and analysis of real-world data sets. Furthermore, a demonstration of how to interpret the results of the principal cluster axes is provided on the analysis of Supreme Court voting data and similarities between the interpretation of competing procedures (e.g., factor analysis and principal component analysis) are provided. In addition to the Supreme Court analysis, we analyze several data sets often used to test cluster analysis procedures, including Fisher's Iris data, Agresti's Crab data, and a data set on glass fragments. Finally, discussion is provided to help determine when the proposed procedure will be the most beneficial to the researcher.

Subtypes of psychopathy in the British household population: findings from the national household survey of psychiatric morbidity

BACKGROUND

Cleckley asserted in 1941 that psychopathic personalities are found in the community as well as prisons. Subtypes of abnormal personality may be identifiable in the general population using contemporary measures of psychopathy.

METHODS

Cluster analysis of PCL:SV scores using the four-facet model with a representative sample of 624 adults aged 16-74 years living in households interviewed in the second of a two-phase survey in Great Britain.

RESULTS

Analysis confirmed an optimum 5-cluster solution and existence in the general population of prototypical or criminal psychopaths, non-psychopathic habitual criminals, and "successful psychopaths". Two additional clusters were identified, one uniquely characterised by impulsive/irresponsible (Facet 3) items and the other by social failure associated with low scores on each facet.

CONCLUSIONS

The study confirmed previously hypothesised and two new subtypes of psychopathy within the general population. This prototypical classification may compliment existing typologies during clinical assessment following further refinement.

Alcohol use trajectories and the ubiquitous cat's cradle: cause for concern?

In recent years, trajectory approaches to characterizing individual differences in the onset and course of substance involvement have gained popularity. Previous studies have sometimes reported 4 prototypic courses: (a) a consistently "low" group, (b) an "increase" group, (c) a "decrease" group, and (d) a consistently "high" group. Although not always recovered, these trajectories are often found, despite these studies varying in the ages of the samples studied and the duration of the observation periods employed. Here, the authors examined the consistency with which these longitudinal patterns of heavy drinking were recovered in a series of latent class growth analyses that systematically varied the age of the sample at baseline, the duration of observation, and the number and frequency of measurement occasions. Data were drawn from a 4-year, 8-wave panel study of college student drinking (N = 3,720). Despite some variability across analyses, there was a strong tendency for these prototypes to emerge regardless of the participants' age at baseline and the duration of observation. These findings highlight potential problems with commonly employed trajectory-based approaches and the need to not over-reify these constructs.

Stability analysis in K-means clustering

This paper develops a new procedure, called stability analysis, for K-means clustering. Instead of ignoring local optima and only considering the best solution found, this procedure takes advantage of additional information from a K-means cluster analysis. The information from the locally optimal solutions is collected in an object by object co-occurrence matrix. The co-occurrence matrix is clustered and subsequently reordered by a steepest ascent quadratic assignment procedure to aid visual interpretation of the multidimensional cluster structure. Subsequently, measures are developed to determine the overall structure of a data set, the number of clusters and the multidimensional relationships between the clusters.

Finding groups using model-based cluster analysis: heterogeneous emotional self-regulatory processes and heavy alcohol use risk

Model-based cluster analysis is a new clustering procedure to investigate population heterogeneity utilizing finite mixture multivariate normal densities. It is an inferentially based, statistically principled procedure that allows comparison of nonnested models using the Bayesian information criterion to compare multiple models and identify the optimum number of clusters. The current study clustered 36 young men and women on the basis of their baseline heart rate and heart rate variability (HRV), chronic alcohol use, and reasons for drinking. Two cluster groups were identified and labeled the high alcohol risk and normative groups. Compared to the normative group, individuals in the high alcohol risk group had higher levels of alcohol use and more strongly endorsed disinhibition and suppression reasons for use. The high alcohol risk group showed significant HRV changes in response to positive and negative emotional and appetitive picture cues, compared to neutral cues. In contrast, the normative group showed a significant HRV change only to negative cues. Findings suggest that individuals with autonomic self-regulatory difficulties may be more susceptible to heavy alcohol use and use of alcohol for emotional regulation.

Psychopathy Subtypes among African American County Jail Inmates

There is evidence that the classification of "psychopath" captures a heterogeneous group of offenders. Although several studies have provided evidence for two distinct psychopath subtypes, these studies have inadequately addressed potentially important ethnic differences. A recent taxonomic study found evidence for primary and secondary psychopath subgroups in a sample of European American offenders (Swogger & Kosson, 2007). The present study used cluster analysis to attempt to replicate those findings in a sample of African American offenders. Results confirm the presence of primary and secondary subtypes in African Americans. However, differences between the clusters obtained in the present and previous studies suggest that caution is warranted in generalizing offender taxonomies across ethnicity.

A New Variable Weighting and Selection Procedure for K-means Cluster Analysis

A variance-to-range ratio variable weighting procedure is proposed. We show how this weighting method is theoretically grounded in the inherent variability found in data exhibiting cluster structure. In addition, a variable selection procedure is proposed to operate in conjunction with the variable weighting technique. The performances of these procedures are demonstrated in a simulation study, showing favorable results when compared with existing standardization methods. A detailed demonstration of the weighting and selection procedure is provided for the well-known Fisher Iris data and several synthetic data sets.