  In the data mining field, association rules are discovered having domain
knowledge specified as a minimum support threshold. The accuracy in setting up
this threshold directly influences the number and the quality of association
rules discovered. Typically, before association rules are mined, a user needs
to determine a support threshold in order to obtain only the frequent item
sets. Having users to determine a support threshold attracts a number of
issues. We propose an association rule mining framework that does not require a
per-set support threshold. Often, the number of association rules, even though
large in number, misses some interesting rules and the rules quality
necessitates further analysis. As a result, decision making using these rules
could lead to risky actions.
