Multidimensional datasets often include categorical information. When most dimensions have categorical information, clustering the dataset as a whole can reveal interesting patterns in the dataset. However, the categorical information is often more useful as a way to partition the dataset: gene expression data for healthy vs. diseased samples or stock performance for common, preferred, or conve...

This paper first illustrates the use of mosaic displays and other graphical methods for the analysis of multiway contingency tables. We then introduce several extensions of mosaic displays designed to integrate graphical methods for categorical data with those used for quantitative data. For example, the scatterplot matrix shows all pairwise (marginal) views of a set of variables in a coherent ...

This paper describes the modeling of a weed infestation risk inference system that implements a collaborative inference scheme based on rules extracted from two Bayesian network classifiers. The first Bayesian classifier infers a categorical variable value for the weed–crop competitiveness using as input categorical variables for the total density of weeds and corresponding proportions of narro...

1 Introduction What types of cars do you own? What are your sources of veterinary information? For what criminal offenses have you been arrested? These are all example questions appearing on surveys where the respondent is prompted to pick any number of responses from a set of variables that summarize this type of survey data have been called multiple-response (or pick any/c) categorical variab...

Multilevel analysis often leads to modeling with multiple latent variables on several levels. While this is less of a problem with Gaussian observed variables, maximum-likelihood (ML) estimation with categorical outcomes presents computational problems due to multidimensional numerical integration. We describe a new method that compared to ML is both computationally efficient and has similar MS...

In this paper, I will report and summarize some preliminary results of two ongoing studies. The aim is to identify problem areas and difficulties of students in elementary data analysis based on preliminary results from the two ongoing studies. The general idea of the two projects is similar. Students took a course in data analysis where they learned to use a software tool, used the tool during...

The present study utilized hierarchical agglomerative cluster (HAC) analysis to categorize users of a popular, web-based computer-assisted pronunciation training (CAPT) program into user types using activity log data. Results indicate an optimal grouping of four types: Reluctant, Point-focused, Optimal, and Engaged. Clustering was determined by aggregate data on seven indicator variables of mix...