Data Mining: Association Rule

1. Data Mining: Association Rule By: Thanh Truong

2. Association Rules In Association Rules, we look at the associations between different items to draw conclusions from. In sales, we look at purchases: Example in book Someone who buys bread is most likely buy milk Someone who buys the book Database System Concepts is quite likely also to buy the book Operating System Concepts.

3. Association Uses When a customer buys a book on-line shop may suggest associated books.

5. (cont�) In grocery stores they can place associated items next to each other. OR They can place at opposite ends of the aisle, with other associated items in between. The store can sell an item at a discounted price, but not the other.

6. Association Notation Association Rules are statement of the form {X1, X2,�, Xn} => Y Means: If we find things in X, then we will most likely find Y

7. Population & Instance An association rule must have an associated population. The population consists of a set of instances. In the grocery example, Population may be all grocery-store purchases Instances are the purchases itself

8. Support Support is the measure of what fraction of the population satisfies both the antecedent and the consequent of the rule. For example, if only .0001 percent of purchases include milk and screwdrivers, then, the support is low for milk => screwdrivers If 50% of purchases of diapers include beer, then we would say the support is high.

9. Confidence Confidence is a measure of how often the consequent is true when the antecedent is true. bread => milk has a confidence of 80% if 80% of the purchases that include bread also includes milk. A rule with a low confidence is not meaningful.

10. Other Types of Association In statistical terms, we can look for correlations between items. So, even if purchases of bread is not correlated with cereal, it would not be reported, even if there was a strong association between the two. Assocation Rule: {bread, butter} => jam Correlation: Someone who buys tea will not buy coffee

11. (cont�) Sequence associations: Time-series data, such as stock prices on a sequence of days Example is the following rule �Whenever bond rates go up, the stock prices go down within 2 days� Using this will help make investment decisions.

12. References Database System Concepts, Fifth Edition, Silberschatz