The Space of Jumping Emerging Patterns and Its Incremental Maintenance Algorithms
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ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
MMAC: A New Multi-Class, Multi-Label Associative Classification Approach
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
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Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
World Wide Web
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Negative Generator Border for Effective Pattern Maintenance
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Direct Discriminative Pattern Mining for Effective Classification
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Minimum description length principle: generators are preferable to closed patterns
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
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PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
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Emerging patterns are itemsets whose frequencies change sharply from one class to the other. PCL is an example of efficient classification algorithms that leverage the prediction power of emerging patterns. It first selects the top-K emerging patterns of each class that match a testing instance, and then uses these selected patterns to decide the class label of the testing instance. We study the impact of the parameter K on the accuracy of PCL. We have observed that in many cases, the value of K is critical to the performance of PCL. This motivates us to develop an algorithm to find the best value of K for PCL. Our results show that finding the best K can improve the accuracy of PCL greatly, and employing incremental frequent itemset maintenance techniques reduces the running time of our algorithm significantly.