Java Data Mining Concepts - Java Data Mining: Strategy, Standard, and Practice - page 160

so the confidence is 2/4 0.5. Confidence is directional; when we

invert the rule, for example, “ If a customer has a checking account, then
they have a savings account ,” we will get a different confidence value.
For the inverted rule, there are only three customers, 1, 3, and 4, that
satisfy antecedent, so the confidence value of this rule is 2/3 0.67.

Note that the inverted rule has greater confidence than the original.
The lift is the ratio between the rule confidence and its expected
confidence. Expected confidence is the frequency of the consequent in
the data. Lift measures how much more likely the consequent is
when an antecedent happens. In this example, there are three
customers, 1, 3 and 4, that have a checking account, so the expected
confidence is 3/5

0.66.
In addition to the rule quality metrics, JDM allows users to specify
taxonomy per attribute (Section 4.5), and settings that include the
maximum number of rules in the model and inclusion or exclusion of
model items. Section 9.7 will discuss more about these setting when
we discuss the API usage.

0.6. Hence lift for this rule is 0.4/0.6

7.4.5 Use Model Content: Explore Rules From the Model

An association model primarily contains the association rules and
their support, confidence, and lift details. Even with the model rule
quality thresholds, this model may contain a large number of rules
based on the number of items and the relationships among these
items. To explore the rules, users often need a filter and to order the
rules to get an interesting or manageable subset. To this end, JDM
provides rule filtering capabilities.
Filtering criteria may include rule support, confidence, and lift
thresholds; inclusion or exclusion of the specified items from the rule
or specific rule components, that is, antecedent and consequent; and
rule or rule component length. Section 9.7 will discuss more about
the various types of filtering criteria using JDM.

7.5 Clustering Problem

7.5.1 Problem Definition: How to Understand Customer

Behavior and Needs

ABCBank has thousands of customers whose profiles and needs
widely differ from each other. ABCBank wants to understand customer
segments to design new products and personalize campaigns to