Mar 6, 2025 · Data discrimination is a comparison of the general features of the target class data objects against the general features of objects from one or multiple contrasting classes. Frequent patterns

Data Characterization refers to the process of classifying, organizing, and analyzing data in a healthcare organization to ensure proper data management and protection. It involves classification, taxonomy, and analytics to handle data consistently and effectively.

Data discrimination is defined as the ability to detect differences between two sets of data based on specific criteria, similar to how individuals detect differences between lights in chromatic discrimination studies.

Aug 22, 2023 · Data Discrimination: It compares common features of class which is under study. It is a comparison of the general features of the target class data objects against the general features of objects from one or multiple contrasting classes.

Nov 23, 2019 · Data Characterization − This refers to summarizing data of class under study. This class under study is called as Target Class. Data Discrimination − It refers to the mapping or classification of a class with some predefined group or class.

Oct 13, 2020 · Data discrimination is a comparison of the general features of a target class data objects with thegeneral features of objects from one or a set of contrasting classes.

Characterization provides a concise and succinct summarization of the given collection of the data, while concept or class comparison (also known as discrimination) provides discriminations comparing two or more collections of data.

description: characterization and discriminatio. customers include bigSpenders and budgetSpenders. It can be useful to describe individual classes and conce. ts in summarized, concise, and yet precise terms. Such descriptions of a class o.

Data Characterization − This refers to summarizing data of class under study. This class under study is called as Target Class. Data Discrimination − It refers to the mapping or classification of a class with some predefined group or class. Frequent patterns are those patterns that occur frequently in transactional data.

characterization can be modified so that the generalization is performed synchronously among all the classes compared. This allows the attributes in all of the classes to be generalized to the same levels of abstraction. Suppose, for instance, that we are given the ABCompany data for sales in 1998 and sales in 1999 and would like to compare