Data Mining Functionalities

What Kinds of Patterns Can Be Mined?
Data mining functionalities are used to specify the kind of patterns to be found in data mining tasks. In general, data mining tasks can be classified into two categories:

• Descriptive mining tasks characterize the general properties of the data in the database. 
• Predictive mining tasks performinference on the current data in order to make predictions.

In many cases, users may have no idea regarding what kinds of patterns in their data may be interesting, and hence may like to search for several different kinds of patterns in parallel. Thus it is important to have a data mining system that can minemultiple kinds of patterns to accommodate different user expectations or applications. Furthermore, data mining systems should be able to discover patterns at various granularity (i.e., different levels of abstraction). Data mining systems should also allow users to specify hints to guide or focus the search for interesting patterns. Because some patterns may not hold for all of the data in the database, a measure of certainty or "trustworthiness" is usually associated with each discovered pattern.
Data mining functionalities, and the kinds of patterns they can discover, are described below.

• Characterization and Discrimination
• Mining Frequent Patterns, Associations, and Correlations
• Classification and Prediction
• Cluster Analysis
• Outlier Analysis
• Evolution Analysis

Concept/Class Description (Characterization and Discrimination)

Data can be associated with classes or concepts. For example, in the AllElectronics store, classes of items for sale include computers and printers, and concepts of customers include bigSpenders and budgetSpenders. It can be 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 descriptions. These descriptions can be derived via

• data characterization, by summarizing the data of the class under study (often called the target class) in general terms, or
• data discrimination, by comparison of the target class with one or a set of comparative classes (often called the contrasting classes), or
• both data characterization and discrimination.

Data characterization 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 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 presented 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 characteristic rules).
Example, Data characterization. A data mining system should be able to produce a description summarizing the characteristics of customers who spend more than $1,000 a year at AllElectronics. The result could be a general profile of the customers, such as they are 40–50 years old, employed, and have excellent credit ratings. The system should allow users to drill down on any dimension, such as on occupation in order to view these customers according to their type of employment.

Data discrimination is a comparison of the general features of target class data objects with the general features of objects from one or a set of contrasting classes. The target and contrasting classes can be specified by the user, and the corresponding data objects retrieved through database queries. For example, the user may like to compare the general features of software products whose sales increased by 10% in the last year with those whose sales decreased by at least 30% during the same period. The methods used for data discrimination are similar to those used for data characterization.
"How are discrimination descriptions output?" The forms of output presentation are similar to those for characteristic descriptions, although discrimination descriptions should include comparative measures that help distinguish between the target and contrasting classes. Discrimination descriptions expressed in rule form are referred to as discriminant rules.