Data Mining Functionalities

Introduction: We have observed various types of databases and information repositories on which data mining can be performed. Let us now examine the kinds of data patterns that 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 and predictive. Descriptive mining tasks characterize the general properties of the data in the database. Predictive mining tasks perform inference on the current data in order to make predictions.

In some 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 mine multiple kinds of patterns to accommodate different user expectations or applications. Furthermore, data mining systems should be able to discover patterns at various granularities (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.

Concept/Class Description: Characterization and Discrimination

Data can be associated with classes or concepts. For example, in the All Electronics store, classes of items for sale include computers and printers, and concepts of customers include big Spenders and budget Spenders. 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

(1) Data characterization, by summarizing the data of the class under study (often called the target class) in general terms, or

(2) Data discrimination, by comparison of the target class with one or a set of comparative classes (often called the contrasting classes), or

(3) 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.

There are several methods for effective data summarization and characterization. The data cube–based OLAP roll-up operation can be used to perform user-controlled data summarization along a specified dimension. An attribute-oriented induction technique can be used to perform data generalization and characterization without step-by-step user interaction.

Mining Frequent Patterns, Associations, and Correlations

Frequent patterns, as the name suggests, are patterns that occur frequently in data. There are many kinds of frequent patterns, including itemsets, subsequences, and substructures. A frequent itemset typically refers to a set of items that frequently appear together in a transactional data set, such as milk and bread. A frequently occurring subsequence, such as the pattern that customers tend to purchase first a PC, followed by a digital camera, and then a memory card, is a (frequent) sequential pattern. A substructure can refer to different structural forms, such as graphs, trees, or lattices, which may be combined with itemsets or subsequences. If a substructure occurs frequently, it is called a (frequent) structured pattern. Mining frequent patterns leads to the discovery of interesting associations and correlations within data.