Page 128 - DMGT308_CUSTOMER_RELATIONSHIP_MANAGEMENT

Unit 5: Closed Loop Marketing

Rule Induction Notes

Try to find rules of the form:
IF <left-hand-side> THEN <right-hand-side>
This is the reverse of a rule-based agent, where the rules are given and the agent must act. Here
the actions are given and we have to discover the rules!
Prevalence = probability that LHS and RHS occur together (sometimes called “support factor,”
“leverage” or “lift”)

Predictability = probability of RHS given LHS (sometimes called “confidence” or “strength”)

Rule-based Algorithms

One of the most well-studied methods for producing sets of classification rules from examples
is rule algorithms. They attempt to cover all instances of each class while excluding instances not
in the class. The main point is that covering algorithms (rule-based) work on a specific class at a
time, ignoring the rest of the classes. For instance, if a rule is desired to classify the weather as
warm, then the covering algorithm attempts to x in the statement.
If x, then class = warm,
With the condition that produces the best probability for the weather to be warm. Covering
algorithms follows these three steps:
1. Generate rule R on training data S
2. Remove the training data covered by rule R
3. Repeat the process

This method can be visualized in the 2D space of instances illustrated in Figure 5.4. Firstly, a rule
is constructed to cover a’s by splitting the space vertically at x = 1.2 and then further splitting it
horizontally at y = 2.6, leading to the rule.
Figure 5.4: Covering Algorithm Demonstration

Source: http://scn.sap.com/docs/DOC-5036763879jdiywsi
If x > 1.2 AND y > 2.6, then class = a

Secondly, the following procedure is used to construct rules to cover b’s:
If x d” 1.2, then class = b
If x > 1.2 AND y d” 2.6, then class = b

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