Data Mining Functionalities
Data mining functionalities are used to specify the kind of patterns to be found in data mining tasks.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.
Concept/Class Description: Characterization and Discrimination
Data can be associated with classes or concepts. For example, in the Electronics store, classes of items for sale include computers and printers, and concepts of customers include bigSpenders and budgetSpenders.
Data characterization
Data characterization is a summarization of the general characteristics or features of a target class of data.
Data discrimination
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.
Mining Frequent Patterns, Associations, and Correlations
Frequent patterns, are patterns that occur frequently in data. There are many kinds of frequent patterns, including itemsets, subsequences, and substructures.
Association analysis
Suppose, as a marketing manager, you would like to determine which items are frequently purchased together within the same transactions.
buys(X,“computer”)=buys(X,“software”) [support=1%,confidence=50%]
where X is a variable representing a customer.Confidence=50% means that if a customer buys a computer, there is a 50% chance that she will buy software as well.
Support=1% means that 1% of all of the transactions under analysis showed that computer and software were purchased together.
Classification and Prediction
Classification is the process of finding a model that describes and distinguishes data classes for the purpose of being able to use the model to predict the class of objects whose class label is unknown.
“How is the derived model presented?” The derived model may be represented in various forms, such as classification (IF-THEN) rules, decision trees, mathematical formulae, or neural networks.
A decision tree is a flow-chart-like tree structure, where each node denotes a test on an attribute value, each branch represents an outcome of the test, and tree leaves represent classes or class distributions.
