Data Mining Functionalities: Characterization, Association and Discrimination

Data-mining functionalities define the kind of patterns a system can discover. Three important ones are:

1. Data characterization

Definition. Data characterization is an automatic summarisation of the general characteristics of a target class of data - the class the user is interested in - drawn from a data set, usually at several abstraction levels.

How it is done: statistical description (count, mean, median, variance), attribute-oriented induction, OLAP roll-up / drill-down on a data cube, or decision-tree induction in which the tree itself is the profile of the class.

Example: a supermarket wants to characterize customers who buy a baby product. The result may read: 80% of these buyers are aged 30-45, 70% shop on weekends and their average bill is Rs. 1,200. This profile supports targeted marketing. Only one class is involved.

2. Data association

Definition. Data association (association rule mining / correlation analysis) discovers interesting associations, correlations, frequent patterns and causal structures among items or attributes in large data sets, without any class label.

Measurements: support and confidence, found by algorithms such as Apriori and FP-growth.

Example - market basket analysis:

$$\{Bread, Butter\} \Rightarrow \{Milk\} \quad (support = 60\%,\ confidence = 100\%)$$

Other examples: the classic diapers and beer rule, web click-stream association (users who open page A also open page B), and correlation between hot weather and soft-drink sales.

3. Data discrimination

Definition. Data discrimination builds a description (discriminant rules) that distinguishes a target class from a contrasting (background) class. It compares the two rather than describing only one.

How it is done: comparison of attribute distributions (box plots, histograms), decision-tree induction on both classes, or statistical significance tests.

Example: in a bank, discriminate good credit-risk customers (target) from bad credit-risk customers (contrast): good-risk customers are usually salaried, aged 30-50, with a debt-to-income ratio below 0.3. Other examples: heavy users vs. light users of a product, buyers vs. non-buyers of a car.

Quick comparison

Functionality	Classes involved	Output	Example
Data characterization	One target class	Profile / summary	Summarize festival shoppers
Data association	None (item to item)	Frequent patterns / rules	Bread + Butter -> Milk
Data discrimination	Target vs. contrast	Discriminant rules	Good vs. bad credit risk