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An asymmetric one-mode data matrix has rows and columns that correspond to the same set of objects. However, the roles of the objects frequently differ for the rows and the columns. For example, in a visual alphabetic confusion matrix from an experimental psychology study, both the rows and columns pertain to letters of the alphabet. Yet the rows correspond to the presented stimulus letter, whereas the columns refer to the letter provided as the response. Other examples abound in psychology, including applications related to interpersonal interactions (friendship, trust, information sharing) in social and developmental psychology, brand switching in consumer psychology, journal citation analysis in any discipline (including quantitative psychology), and free association tasks in any subarea of psychology. When seeking to establish a partition of the objects in such applications, it is overly restrictive to require the partitions of the row and column objects to be identical, or even the numbers of clusters for the row and column objects to be the same. This suggests the need for a biclustering approach that simultaneously establishes separate partitions of the row and column objects. We present and compare several approaches for the biclustering of one-mode matrices using data sets from the empirical literature. A suite of MATLAB m-files for implementing the procedures is provided as a Web supplement with this article. [ABSTRACT FROM AUTHOR]

The maximum cardinality subset selection problem requires finding the largest possible subset from a set of objects, such that one or more conditions are satisfied. An important extension of this problem is to extract multiple subsets, where the addition of one more object to a larger subset would always be preferred to increases in the size of one or more smaller subsets. We refer to this as the multiple subset maximum cardinality selection problem ( MSMCSP). A recently published branch-and-bound algorithm solves the MSMCSP as a partitioning problem. Unfortunately, the computational requirement associated with the algorithm is often enormous, thus rendering the method infeasible from a practical standpoint. In this paper, we present an alternative approach that successively solves a series of binary integer linear programs to obtain a globally optimal solution to the MSMCSP. Computational comparisons of the methods using published similarity data for 45 food items reveal that the proposed sequential method is computationally far more efficient than the branch-and-bound approach. [ABSTRACT FROM AUTHOR]

Background Variability in sensitivity to the acute effects of alcohol is an important risk factor for the development of alcohol use disorder ( AUD). The most commonly used retrospective self-report measure of sensitivity, the Self-Rating of the Effects of Alcohol (SRE) form, queries a limited number of alcohol effects and relies on respondents' ability to recall experiences that might have occurred in the distant past. Here, we investigated the construct validity of an alternative measure that queries a larger number of alcohol effects, the Alcohol Sensitivity Questionnaire ( ASQ), and compared it to the SRE in predicting momentary subjective responses to an acute dose of alcohol. Methods Healthy young adults ( N = 423) completed the SRE and the ASQ and then were randomly assigned to consume either alcohol or a placebo beverage (between-subjects manipulation). Stimulation and sedation (Biphasic Alcohol Effects Scale) and subjective intoxication were measured multiple times after drinking. Results Hierarchical linear models showed that the ASQ reliably predicted each of these outcomes following alcohol but not placebo consumption, provided unique prediction beyond that associated with differences in recent alcohol involvement, and was preferred over the SRE (in terms of model fit) in direct model comparisons of stimulation and sedation. Conclusions The ASQ compared favorably with the better-known SRE in predicting increased stimulation and reduced sedation following an acute alcohol challenge. The ASQ appears to be a valid self-report measure of alcohol sensitivity and therefore holds promise for identifying individuals at-risk for AUD and related problems. [ABSTRACT FROM AUTHOR]

Some individuals “change” more dramatically than others when intoxicated, and the nature and magnitude of these changes can result in harmful outcomes. This study utilized reports (N = 374) of participants’ “typical” five-factor model (FFM) characteristics across sober and intoxicated states and assessed the degree to which these reports could be grouped into meaningful clusters, as well as the association of cluster membership with negative alcohol-related consequences. Results from finite mixture model clustering revealed a four cluster solution. Cluster 1, “Hemingway,” was the largest and defined by intoxication-related decreases in Conscientiousness and Intellect that were below average; Cluster 2, “Mary Poppins,” was defined by being high in Agreeableness when sober, decreasing less than average in Conscientiousness and Intellect and increasing more than average in Extraversion when drunk; Cluster 3, “Mr. Hyde,” reported larger drunk decreases in Conscientiousness and Intellect and smaller increases in Extraversion; Cluster 4, “The Nutty Professor,” was defined by being low in Extraversion when sober, increasing more than average in Extraversion and decreasing less than average in Conscientiousness when drunk. Cluster membership was associated with experiencing more alcohol consequences. These results support use of the FFM to characterize clinically meaningful subgroups of sober-to-drunk differences in trait expression. [ABSTRACT FROM AUTHOR]

Arguments are proposed that researchers using longitudinal data should consider more and less complex statistical model alternatives to their initially chosen techniques in an effort to "right-size" the model to the data at hand. Such model comparisons may alert researchers who use poorly fitting, overly parsimonious models to more complex, better-fitting alternatives and, alternatively, may identify more parsimonious alternatives to overly complex (and perhaps empirically underidentified and/or less powerful) statistical models. A general framework is proposed for considering (often nested) relationships between a variety of psychometric and growth curve models. A 3-step approach is proposed in which models are evaluated based on the number and patterning of variance components prior to selection of better-fitting growth models that explain both mean and variation-covariation patterns. The orthogonal free curve slope intercept (FCSI) growth model is considered a general model that includes, as special cases, many models, including the factor mean (FM) model (McArdle & Epstein, 1987), McDonald's (1967) linearly constrained factor model, hierarchical linear models (HLMs), repeatedmeasures multivariate analysis of variance (MANOVA), and the linear slope intercept (linearSI) growth model. The FCSI model, in turn, is nested within the Tuckerized factor model. The approach is illustrated by comparing alternative models in a longitudinal study of children's vocabulary and by comparing several candidate parametric growth and chronometric models in a Monte Carlo study. [ABSTRACT FROM AUTHOR]

The monotone homogeneity model (MHM-also known as the unidimensional monotone latent variable model) is a nonparametric IRT formulation that provides the underpinning for partitioning a collection of dichotomous items to form scales. Ellis (Psychometrika 79:303-316, , doi:) has recently derived inequalities that are implied by the MHM, yet require only the bivariate (inter-item) correlations. In this paper, we incorporate these inequalities within a mathematical programming formulation for partitioning a set of dichotomous scale items. The objective criterion of the partitioning model is to produce clusters of maximum cardinality. The formulation is a binary integer linear program that can be solved exactly using commercial mathematical programming software. However, we have also developed a standalone branch-and-bound algorithm that produces globally optimal solutions. Simulation results and a numerical example are provided to demonstrate the proposed method. [ABSTRACT FROM AUTHOR]

One of the most important distinctions that must be made in clustering research is the difference between models (or problems) and the methods for solving those problems. Nowhere is this more evident than with the evaluation of the popular affinity propagation algorithm (apcluster.m), which is a MATLAB implementation of a neural clustering method that has received significant attention in the biological sciences and other disciplines. Several authors have undertaken comparisons of apcluster.m with methods designed for models that fall within the class of uncapacitated facility location problems (UFLPs). These comparative models include the p-center (or K-center) model and, more importantly, the p-median (or K-median) model. The results across studies are conflicting and clouded by the fact that, although similar, the optimization model underlying apcluster.m is slightly different from the p-median model and appreciably different from the pcenter model. In this paper, we clarify that apcluster.m is actually a heuristic for a 'maximization version' of another model in the class of UFLPs, which is known as the simple plant location problem (SPLP). An exact method for the SPLP is described, and the apcluster.m program is compared to a fast heuristic procedure (sasplp.m) in both a simulation experiment and across numerous datasets from the literature. Although the exact method is the preferred approach when computationally feasible, both apcluster.m and sasplp.m are efficient and effective heuristic approaches, with the latter slightly outperforming the former in most instances. [ABSTRACT FROM AUTHOR]

An introduction to the journal is presented on topics including clustering of time series data; approach to data reduction in the context of classification; and construction of cognitive diagnostic tests.