categorical data


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Related to categorical data: Numerical data

categorical data

A term used in the context of a clinical trial for data evaluated by sorting values—e.g., severe, moderate and mild—into various categories.
References in periodicals archive ?
Clustering Categorical Data. Let X = {[X.sub.1], [X.sub.2], ..., [X.sub.N]} be a categorical dataset with N data points [X.sub.i].
He, "Density-based clustering algorithm for numerical and categorical data with mixed distance measure methods," Control Theory and Applications, vol.
Categorical data, also called nominal data, are an either/or type of data: males or females, fell or did not fall, patients had a total knee arthroplasty or a total hip arthroplasty (TKA/THA) or they did not.
The process of converting numerical to categorical data is called binning or discretization [11].
In a complementary line of research, the authors have developed a model for the analysis of categorical data with an endogenous weighting system (see Herrero & Villar, 2013) permitting one to address this problem from a slightly different viewpoint (see Herrero, Mendez & Villar, 2014).
Although it is assumed in categorical data analysis that each observed variable has an underlying scale that is both continuous and normally distributed, especially in scales with five or more categories (Byrne, 2006), problems begin to emerge as the observed item distributions diverge widely from a normal distribution.
Six aspects of the urinalysis were measured as categorical data. Information provided by the researchers stated the aspects of the urinalysis that were measured but not why they were ordinal measurements.
To test the hypotheses, an analysis of categorical data was used.
The obtained data are usually "categorical data" or "ordinal categorical data," which are collected based on a scale of "strongly agree," "agree," "have no opinion," "disagree," and "strongly disagree." Because most data in traditional statistical methods are interval data, researchers often assign these ordinal categorical data a score first, convert them into interval data, and then conduct further statistical analyses, such as factor analysis, principal analysis, and discriminate analysis.
However, methods for imputing categorical data are still experimental in some software releases.
The topics include a survey of partitional and hierarchical clustering algorithms, non-negative matrix factorizations for clustering, clustering categorical data, time-series data clustering, network clustering, uncertain data clustering algorithms, semi-supervised clustering, and educational and software resources for data clustering.