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A cost centre is a department within a business to which costs can be allocated. The term includes departments which do not produce directly but they incur costs to the business, [1] when the manager and employees of the cost centre are not accountable for the profitability and investment decisions of the business but they are responsible for some of its costs.
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]
Cost classifications based on functions, activities, products, processes and on the information needs of the organization in its planning and control. Cost classifications based on the types of transactions. Combines objective and subjective assessment of costs contributing to a standard result. Aims to present a 'true and fair' view of ...
Industry classification or industry taxonomy is a type of economic taxonomy that classifies companies, organizations and traders into industrial groupings based on similar production processes, similar products, or similar behavior in financial markets.
An effective cost allocation methodology enables an organization to identify what services are being provided and what they cost, to allocate costs to business units, and to manage cost recovery. Under this model, both the service provider and its respective consumers become aware of their service requirements and usage and how they directly ...
In statistics, where classification is often done with logistic regression or a similar procedure, the properties of observations are termed explanatory variables (or independent variables, regressors, etc.), and the categories to be predicted are known as outcomes, which are considered to be possible values of the dependent variable.
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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.