Association Mining to Correlation Analysis
Introduction: Most association rule mining algorithms employ a support-confidence framework. Often, many interesting rules can be found using low support thresholds. Although minimum support and confidence thresholds help weed out or exclude the exploration of a good number of uninteresting rules, many rules so generated are still not interesting to the users. Unfortunately, this is especially true when mining at low support thresholds or mining for long patterns. This has been one of the major bottlenecks for successful application of association rule mining.
In this section, we first look at how even strong association rules can be uninteresting and misleading. We then discuss how the support-confidence framework can be supplemented with additional interestingness measures based on statistical significance and correlation analysis.
Strong Rules Are Not Necessarily Interesting: An Example
Whether or not a rule is interesting can be assessed either subjectively or objectively. Ultimately, only the user can judge if a given rule is interesting, and this judgment, being subjective, may differ from one user to another. However, objective interestingness measures, based on the statistics “behind” the data, can be used as one step toward the goal of weeding out uninteresting rules from presentation to the user.