C4.5: programs for machine learning
C4.5: programs for 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
Making use of the most expressive jumping emerging patterns for classification
Knowledge and Information Systems
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
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
Hiding emerging patterns with local recoding generalization
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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Classification is an important data mining problem Emerging Patterns (EPs) are itemsets whose supports change significantly from one data class to another Previous studies have shown that classifiers based on EPs are competitive to other state-of-the-art classification systems In this paper, we propose a new type of Emerging Patterns, called Maximal Emerging Patterns (MaxEPs), which are the longest EPs satisfying certain constraints MaxEPs can be used to condense the vast amount of information, resulting in a significantly smaller set of high quality patterns for classification We also develop a new “overlapping” or “intersection” based mechanism to exploit the properties of MaxEPs Our new classifier, Classification by Maximal Emerging Patterns (CMaxEP), combines the advantages of the Bayesian approach and EP-based classifiers The experimental results on 36 benchmark datasets from the UCI machine learning repository demonstrate that our method has better overall classification accuracy in comparison to JEP-classifier, CBA, C5.0 and NB.