Machine Learning
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
Extending naïve Bayes classifiers using long itemsets
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
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
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Building Behaviour Knowledge Space to Make Classification Decision
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Contrast pattern mining and its applications
ADC '10 Proceedings of the Twenty-First Australasian Conference on Database Technologies - Volume 104
Krimp: mining itemsets that compress
Data Mining and Knowledge Discovery
Compression picks item sets that matter
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Building a more accurate classifier based on strong frequent patterns
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Efficiently finding the best parameter for the emerging pattern-based classifier PCL
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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Emerging patterns (EPs) are knowledge patterns capturing contrasts between data classes. In this paper, we propose an information-based approach for classification by aggregating emerging patterns. The constraint-based EP mining algorithm enables the system to learn from large-volume and high-dimensional data; the new approach for selecting representative EPs and efficient algorithm for finding the EPs renders the system high predictive accuracy and short classification time. Experiments on many benchmark datasets show that the resulting classifiers have good overall predictive accuracy, and are often also superior to other state-of-the-art classification systems such as C4.5, CBA and LB.