On the Approximation of Shortest Common Supersequencesand Longest Common Subsequences
SIAM Journal on Computing
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
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
Mining border descriptions of emerging patterns from dataset pairs
Knowledge and Information Systems
Mining decision rules on data streams in the presence of concept drifts
Expert Systems with Applications: An International Journal
On the Complexity of Constraint-Based Theory Extraction
DS '09 Proceedings of the 12th International Conference on Discovery Science
Contrasting Sequence Groups by Emerging Sequences
DS '09 Proceedings of the 12th International Conference on Discovery Science
Transactions on rough sets XII
Comprehending performance from real-world execution traces: a device-driver case
Proceedings of the 19th international conference on Architectural support for programming languages and operating systems
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Emerging patterns have been studied as a useful type of pattern for the diagnosis and understanding of diseases based on the analysis of gene expression profiles. They are useful for capturing interactions among genes (or other biological entities), for capturing signature patterns for disease subtypes, and deriving potential disease treatment plans, etc. In this paper we study the complexity of finding emerging patterns (with the highest frequency). We first show that the problem is MAX SNP-hard. This implies that polynomial time approximation schemes do not exist for the problem unless P = NP. We then prove that for any constant δ logδn in polynomial time unless NP ⊆ DTIME[2polylog n], where n is the number of positions in a pattern.