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
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Exploring constraints to efficiently mine emerging patterns from large high-dimensional datasets
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
CAEP: Classification by Aggregating Emerging Patterns
DS '99 Proceedings of the Second International Conference on Discovery Science
Fast Algorithms for Mining Emerging Patterns
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
A Bayesian approach to use emerging patterns for classification
ADC '03 Proceedings of the 14th Australasian database conference - Volume 17
IEEE Transactions on Knowledge and Data Engineering
Using Emerging Patterns to Construct Weighted Decision Trees
IEEE Transactions on Knowledge and Data Engineering
World Wide Web
Jumping emerging patterns with negation in transaction databases - Classification and discovery
Information Sciences: an International Journal
Discovering Relational Emerging Patterns
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
Emerging Pattern Based Classification in Relational Data Mining
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Efficient Discovery of Top-K Minimal Jumping Emerging Patterns
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Mining Class Contrast Functions by Gene Expression Programming
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Maximum item first pattern growth for mining frequent patterns
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Mining discriminative items in multiple data streams
World Wide Web
Contrast pattern mining and its applications
ADC '10 Proceedings of the Twenty-First Australasian Conference on Database Technologies - Volume 104
Transactions on rough sets XII
Mining contrast inequalities in numeric dataset
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Efficient mining of jumping emerging patterns with occurrence counts for classification
Transactions on rough sets XIII
Mining emerging patterns by streaming feature selection
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Emerging patterns (EPs), namely itemsets whose supports change significantly from one class to another, were recently proposed to capture multi-attribute contrasts between data classes, or trends over time. Previous studies show that EP/JEP(jumping emerging patterns) - based classifiers such as CAEP[2] and JEP-classifier[6] have good overall predictive accuracy. But they suffer from the huge number of mined EPs/JEPs, which makes the classifiers complex.In this study, we propose a special type of EP, essential jumping emerging patterns (eJEPs), which are believed to be high quality patterns with the most differentiating power and thus are sufficient for building accurate classifiers. Existing algorithms such as border-based algorithms and consEPMiner[7] can not directly mine such eJEPs. We present a new single-scan algorithm to effectively mine eJEPs of both data classes (both directions). Experimental results show that the classifier based exclusively on eJEPs, which uses much fewer JEPs than JEP-classifier, achieves the same or higher testing accuracy and is often also superior to other state-of-the-art classification systems such as C4.5a nd CBA.