Efficient Discovery of Top-K Minimal Jumping Emerging Patterns

Jumping emerging patterns, like other discriminative patterns, help to understand differences between decision classes and build accurate classifiers. Since their discovery is usually time-consuming and pruning with minimum support may require several adjustments, we consider the problem of finding top-kminimal jumping emerging patterns. We describe the approach based on a CP-Tree that gradually raises minimum support during mining. Also, a general strategy for pruning non-minimal patterns and their descendants is proposed. We employ the concept of attribute set dependence to test pattern minimality. A two and multiple class version of the problem is discussed. Experiments evaluate pruning capabilities and execution time.

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