A theory of diagnosis from first principles
Artificial Intelligence
Identifying the Minimal Transversals of a Hypergraph and Related Problems
SIAM Journal on Computing
On the complexity of dualization of monotone disjunctive normal forms
Journal of Algorithms
Data mining, hypergraph transversals, and machine learning (extended abstract)
PODS '97 Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
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
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Making Use of the Most Expressive Jumping Emerging Patterns for Classification
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
On the Complexity of Generating Maximal Frequent and Minimal Infrequent Sets
STACS '02 Proceedings of the 19th Annual Symposium on Theoretical Aspects of Computer Science
Evaluation of an Algorithm for the Transversal Hypergraph Problem
WAE '99 Proceedings of the 3rd International Workshop on Algorithm Engineering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Discrete Applied Mathematics - Special issue: Discrete algorithms and optimization, in honor of professor Toshihide Ibaraki at his retirement from Kyoto University
World Wide Web
A Data Mining Formalization to Improve Hypergraph Minimal Transversal Computation
Fundamenta Informaticae
A note on systems with max--min and max-product constraints
Fuzzy Sets and Systems
Computational aspects of monotone dualization: A brief survey
Discrete Applied Mathematics
Lower bounds for three algorithms for transversal hypergraph generation
Discrete Applied Mathematics
Mining Class Contrast Functions by Gene Expression Programming
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Efficient incremental mining of contrast patterns in changing data
Information Processing Letters
Lower bounds for three algorithms for the transversal hypergraph generation
WG'07 Proceedings of the 33rd international conference on Graph-theoretic concepts in computer science
Parallel computation of the minimal elements of a poset
Proceedings of the 4th International Workshop on Parallel and Symbolic Computation
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
Mining contrast inequalities in numeric dataset
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Left-to-Right Multiplication for Monotone Boolean Dualization
SIAM Journal on Computing
How to apply SAT-solving for the equivalence test of monotone normal forms
SAT'11 Proceedings of the 14th international conference on Theory and application of satisfiability testing
Toward a fair review-management system
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Discovery of minimal unsatisfiable subsets of constraints using hitting set dualization
PADL'05 Proceedings of the 7th international conference on Practical Aspects of Declarative Languages
Mining emerging patterns by streaming feature selection
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Estimating entity importance via counting set covers
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
A Data Mining Formalization to Improve Hypergraph Minimal Transversal Computation
Fundamenta Informaticae
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Computing the minimal transversals of a hypergraph isan important problem in computer science that has significantapplications in data mining. In this paper, we present anew algorithm for computing hypergraph transversals andhighlight their close connection to an important class ofpatterns known as emerging patterns. We evaluate our techniqueon a number of large datasets and show that it out-performsprevious approaches by a factor of 9-29 times.