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Data classification is the process of organizing data into categories based on attributes like file type, content, or metadata. The data is then assigned class labels that describe a set of attributes for the corresponding data sets. The goal is to provide meaningful class attributes to former less structured information.
Data classification may refer to: Data classification (data management) Data classification (business intelligence) Classification (machine learning), classification of data using machine learning algorithms; Assigning a level of sensitivity to classified information; In computer science, the data type of a piece of data
The first step in doing a data classification is to cluster the data set used for category training, to create the wanted number of categories. An algorithm, called the classifier, is then used on the categories, creating a descriptive model for each. These models can then be used to categorize new items in the created classification system. [2]
SOI is not a classification of data per se. It is defined under the Security of Information Act , and unauthorised release of such information constitutes a higher breach of trust, with a penalty of up to life imprisonment if the information is shared with a foreign entity or terrorist group.
Urdu Informatics (Urdu: اردو اطلاعیات) relates to the research and contributions in bringing the utilities and usage of Urdu to the modern information and ...
An algorithm that implements classification, especially in a concrete implementation, is known as a classifier. The term "classifier" sometimes also refers to the mathematical function, implemented by a classification algorithm, that maps input data to a category. Terminology across fields is quite varied.
Pages in category "Data processing" ... Data classification (business intelligence) Data entry; Data entry clerk; Data onboarding; Data processing (disambiguation)
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.