In general data can be associated with classes or concepts.
For example, if we take the supermarket system we can have classes like customers, employees, item for sale, etc...
And we can have concepts of customers include big spenders and budget spenders.
* Data characterization is a summarization of the general characteristics or features of a class of data. The data corresponding to the user-specified class are typically collected by a database query.
For example, to study the last month sales of the supermarket can be collected by executing a SQL query. The output of data characterization can be presented in various forms. Such as, pie charts, bar charts, multidimensional tables and etc …
A pie chart
A Bar chart
Example 1
A data mining system should be able to produce a description summarizing of the characteristics of customers who spend more than Rs.1000/= per month in a super market.
We could get the results by executing a SQL query. The result could be a general profile of the customers, such as they are 20-30 years old, employed, and have excellent credit ratings.
* Data discrimination is a comparison of the general features of target class data object with the features of objects from one or a set of contrasting classes.
The target and contrasting classes can be classified by the user and the corresponding data objects retrieved through database query.
For example, Manager of a supermarket likes to compare sales item which one’s sales increased by 10% with which one’s sales decreased by 20%. The forms of output presentation are similar to those for characteristics descriptions, although discrimination descriptions should include comparative measures that help distinguish between the target and contrasting classes.
Example 2
A data mining system should be able to compare two groups of super market customers, such as those who shop regularly versus those who shop rarely.
The resulting description provides a general comparative profile of the customers, such as 80% of the customers who shop regularly have the higher level of income and 10% of the customers who shop rarely have the lower level of income.