Databases Reference
In-Depth Information
When typically developed, rules do not
necessarily
describe causality.
Sometimes, the association might be causal; for example, if
Someone eats salty peanuts, then drinks beer.
or
Someone drinks beer, then becomes inebriated.
there may be a causal relationship. On the other hand, if
A rooster grows, then the sun rises.
or
Someone wears a 'lucky' shirt, then wins a lottery.
there may not be a causal relationship. Recognizing true causal relationships
would greatly enhances the decision value of data mining results.
4.1 Not Considering Causality Can Lead to Poor Decisions
Association rules are used is to aid in making retail decisions. However, simple
association rules may lead to errors. Errors might occur; either if causality is
recognized where there is no causality; or if the direction of the causal rela-
tionship is wrong [16, 26]. Errors might occur; either if causality is recognized
where there is no causality; or if the direction of the causal relationship is
wrong. For example, if
A study of past customers shows that 94% are sick.
•
Is it the following rule?
Our customers are sick, so they buy from us.
•
Is it the following complementary rule?
If people use our products, they become sick.
From a decision-making viewpoint, it is not enough to know that
People both buy our products and are sick.
What is needed is knowledge of what causes what, if at all.
4.2 Inherently Uncertain Recognition
Recognizing many things with absolute certainty is problematic. As this is
the case, our causal understanding is based on a foundation of inherent un-
certainty and incompleteness. Consequently, causal reasoning models must
accommodate inherent ambiguity. Some possible factors are [15]:
•
Quantum physics
•
Chaos theory
•
Observer interference
