A modeling language for mathematical programming
Management Science
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
Transversing itemset lattices with statistical metric pruning
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Making use of the most expressive jumping emerging patterns for classification
Knowledge and Information Systems
Detecting Group Differences: Mining Contrast Sets
Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
On detecting differences between groups
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining statistically important equivalence classes and delta-discriminative emerging patterns
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Direct mining of discriminative and essential frequent patterns via model-based search tree
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Low-Support Discriminative Patterns from Dense and High-Dimensional Data
IEEE Transactions on Knowledge and Data Engineering
| Hi-index | 0.00 |
Discriminative pattern mining looks for association patterns that occur more frequently in one class than another and has important applications in many areas including finding biomarkers in biomedical data. However, finding such patterns is challenging because higher order combinations of variables may show high discrimination even when single variables or lower-order combinations show little or no discrimination. Thus, generating such patterns is important for evaluating discriminative pattern mining algorithms and better understanding the nature of discriminative patterns. To that end, we describe how such patterns can be defined using mathematical constraints which are then solved with widely available software that generates solutions for the resulting optimization problem. We present a basic formulation of the problem obtained from a straightforward translation of the desired pattern characteristics into mathematical constraints, and then show how the pattern generation problem can be reformulated in terms of the selection of rows from a truth table. This formulation is more efficient and provides deeper insight into the process of creating higher order patterns. It also makes it easy to define patterns other than just those based on the conjunctive logic used by traditional association and discriminant pattern analysis.