- [Narrator] In order to know when to use association rules,
we need to understand their strengths and weaknesses.
In terms of strengths,
association rules are very good with big data:
the larger the better.
The more occurrences there are of a particular rule
within a dataset, the more interesting the rule is,
and the more likely it is that the rule is actionable.
The simple if/then syntax of an association rule
makes it very easy to understand and communicate.
The left hand side of a rule, which is known as antecedent,
is the condition that needs to be met,
while the right hand side of the rule,
which is known as a consequence, is expected result
of meeting the condition.
Association rules are very useful for data mining tasks,
such as discovering unexpected or previously hidden patterns
within a dataset.
While commonly used for market basket analysis,
association rules are useful
in identifying patterns of co-occurrence
in a wide variety of domains.
Association rules have been used
to identify frequently occurring patterns of DNA
and protein sequences in cancer cells.
They have also been used to find patterns of medical claims
that occur in combination with fraudulent credit card use,
and they have also been used
to identify patterns of behavior
that proceed customer changes to cable TV subscriptions.
There are some weaknesses inherent with association rules
as well.
Association rules are not very useful
when working with small datasets.
The fewer the number of occurrences of a rule,
the less interesting it is.
This is because rules that occur infrequently
could simply occur by chance
and are typically not actionable.
It is sometimes difficult to properly separate true insight
from common sense when evaluating association rules.
Most rules generated during association rule mining
are trivial or inexplicable.
Identifying rules which are truly actionable
often requires domain knowledge
and a significant amount of effort.
Because one person's trash is another's treasure,
the insight derived from association rules
can be very subjective.
If care is not taken,
one can easily draw misleading or incorrect conclusions
from random patterns in the data.