Ans. Data can be associated with class or concept. For example, in the All Electronics store, classes of items for. sale include computers and printers and concepts of customers include big spender b and budget spender. It can. useful to describe individual classes and concepts in summarized, concise, and yet precise terms. Such descriptions of a class or a concept are called class/concept-description. These descriptions can be derived via-
Data Characterization –
It is a summarization of the general characteristics or features of a target class of data. The data corresponding to the user-specified class are typically collected by a database query: For example, to study the characteristics of software, products whose sales are increased by 10% in the last year, the data related to such products can be collected by executing an SQL query. The output of data characterization can be represented in various forms. Examples include pie charts, bar charts, curves, multidimensional data cubes, and multidimensional tables, including crosstabs. The resulting descriptions can also be. presented as generalized relations or in rule form (called a characteristic rule)
Data Discrimination –
It is a comparison of general features of target class data objects with the general features from one or a set of contrasting classes. The target and contrasting classes can be specified by the user, the corresponding data objects retrieved through database queries. For example, the user may like to compare the general features of software products whose sales are increased by 10% in the last year with those whose, sales are decreased by at least 30% during the same period: The method used for data discrimination are similar to those used for data characterization.
