The lift value is a measure of importance of a rule. By
using rule filters, you can define the desired lift range in the settings.
The lift value of an association rule is the ratio of the confidence
of the rule and the expected confidence of the rule. The expected
confidence of a rule is defined as the product of the support values
of the rule body and the rule head divided by the support of the rule
body.
The confidence value is defined as the ratio of the support of
the joined rule body and rule head divided by the support of the rule
body.
The lift value of a rule is defined like this:
lift = confidence / expected_confidence =
confidence / ( s(body) * s(head) / s(body) ) = confidence / s(head)
Where:
- s(body)
- Is the support of the rule body
- s(head)
- Is the support of the rule head
The expected confidence is identical to the support of the rule
head. It is assumed in the definition of the expected confidence that
there is no statistic relation between the rule body and the rule
head. This means that the occurrence of the rule body does not influence
the probability for the occurrence of the rule head and vice versa.
The lift is a measure for the deviation of the rule from the model
of statistic independency of the rule body and rule head. The lift
is a value between 0 and infinity:
- A lift value greater than 1 indicates that the rule body and the
rule head appear more often together than expected, this means that
the occurrence of the rule body has a positive effect on the occurrence
of the rule head.
- A lift smaller than 1 indicates that the rule body and the rule
head appear less often together than expected, this means that the
occurrence of the rule body has a negative effect on the occurrence
of the rule head.
- A lift value near 1 indicates that the rule body and the rule
head appear almost as often together as expected, this means that
the occurrence of the rule body has almost no effect on the occurrence
of the rule head.
You can see the lift value of an association rule with IM Visualization.
You can extract it from a rule model by using the
DM_getRules table
function.