Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Making use of the most expressive jumping emerging patterns for classification
Knowledge and Information Systems
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining N-most Interesting Itemsets
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
An Efficient Single-Scan Algorithm for Mining Essential Jumping Emerging Patterns for Classification
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
TFP: An Efficient Algorithm for Mining Top-K Frequent Closed Itemsets
IEEE Transactions on Knowledge and Data Engineering
TSP: Mining top-k closed sequential patterns
Knowledge and Information Systems
Mining border descriptions of emerging patterns from dataset pairs
Knowledge and Information Systems
IEEE Transactions on Knowledge and Data Engineering
Mining statistically important equivalence classes and delta-discriminative emerging patterns
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Local projection in jumping emerging patterns discovery in transaction databases
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Attribute set dependence in apriori-like reduct computation
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Transactions on rough sets XII
Top-N minimization approach for indicative correlation change mining
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
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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.