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Data classification can be viewed as a multitude of labels that are used to define the type of data, especially on confidentiality and integrity issues. [1] Data classification is typically a manual process; however, there are tools that can help gather information about the data. [2] Data sensitivity levels are often proposed to be considered. [2]
Pragmatic classification (and functional [39] and teleological classification) is the classification of items which emphasis the goals, purposes, consequences, [40] interests, values and politics of classification. It is, for example, classifying animals into wild animals, pests, domesticated animals and pets.
In biology, taxonomic rank (which some authors prefer to call nomenclatural rank [1] because ranking is part of nomenclature rather than taxonomy proper, according to some definitions of these terms) is the relative or absolute level of a group of organisms (a taxon) in a hierarchy that reflects evolutionary
In information science and ontology, a classification scheme is an arrangement of classes or groups of classes. The activity of developing the schemes bears similarity to taxonomy, but with perhaps a more theoretical bent, as a single classification scheme can be applied over a wide semantic spectrum while taxonomies tend to be devoted to a single topic.
Classification is a part of many different kinds of activities and is studied from many different points of view including medicine, philosophy, law, anthropology, biology, taxonomy, cognition, communications, knowledge organization, psychology, statistics, machine learning, economics and mathematics.
The concept of data type is similar to the concept of level of measurement, but more specific. For example, count data requires a different distribution (e.g. a Poisson distribution or binomial distribution) than non-negative real-valued data require, but both fall under the same level of measurement (a ratio scale).
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Early work on statistical classification was undertaken by Fisher, [1] [2] in the context of two-group problems, leading to Fisher's linear discriminant function as the rule for assigning a group to a new observation. [3] This early work assumed that data-values within each of the two groups had a multivariate normal distribution.