Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
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
Making use of the most expressive jumping emerging patterns for classification
Knowledge and Information Systems
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Carpenter: finding closed patterns in long biological datasets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
FARMER: finding interesting rule groups in microarray datasets
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
High Confidence Rule Mining for Microarray Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Top-down mining of frequent closed patterns from very high dimensional data
Information Sciences: an International Journal
Using Highly Expressive Contrast Patterns for Classification - Is It Worthwhile?
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Mining Predictive k-CNF Expressions
IEEE Transactions on Knowledge and Data Engineering
Mining disjunctive minimal generators with TitanicOR
Expert Systems with Applications: An International Journal
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We investigate in this paper the problem of mining disjunctive emerging patterns in high-dimensional biomedical datasets. Disjunctive emerging patterns are sets of features that are very frequent among samples of a target class, cases in a case-control study, for example, and are very rare among all other samples. We, for the very first time, demonstrate that this problem can be solved using minimal transversals in a hypergraph. We propose a new divide-and-conquer algorithm that enables us to efficiently compute disjunctive emerging patterns in parallel and distributed environments. We conducted experiments using real-world microarray gene expression datasets to assess the performance of our approach. Our results show that our approach is more efficient than the state-of-the-art solution available in the literature. In this sense, we contribute to the area of bioinformatics and data mining by providing another useful alternative to identify patterns distinguishing samples with different class labels, such as those in case-control studies, for example.