Date Added: Aug 2009
Mining for association rules and frequent patterns is a central activity in data mining. However, most existing algorithms are only moderately suitable for real-world scenarios. Most strategies use parameters like minimum support, for which it can be very difficult to define a suitable value for unknown datasets. Since most untrained users are unable or unwilling to set such technical parameters, the authors address the problem of replacing the minimum-support parameter with top-n strategies. In the authors' paper, they start by extending a top-n implementation of the ECLAT algorithm to improve its performance by using heuristic search strategy optimizations.