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
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
CAEP: Classification by Aggregating Emerging Patterns
DS '99 Proceedings of the Second International Conference on Discovery Science
On detecting differences between groups
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Regression Modeling Strategies
Regression Modeling Strategies
Formal Concept Analysis: Foundations and Applications (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence)
World Wide Web
Efficient Mining of Contrast Patterns and Their Applications to Classification
ICISIP '05 Proceedings of the 2005 3rd International Conference on Intelligent Sensing and Information Processing
Mining influential attributes that capture class and group contrast behaviour
Proceedings of the 17th ACM conference on Information and knowledge management
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
Discovering Emerging Graph Patterns from Chemicals
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Difference detection between two contrast sets
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Strong Compound-Risk Factors: Efficient Discovery Through Emerging Patterns and Contrast Sets
IEEE Transactions on Information Technology in Biomedicine
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We define a class of patterns generalizing the jumping emerging patterns which have been used successfully for classification problems but which are often absent in complex or sparse databases and which are often very specific. In supervised learning, the objects in a database are classified a priori into one class called positive - a target class - and the remaining classes, called negative. Each pattern, or set of attributes, has support in the positive class and in the negative class, and the ratio of these is the emergence of that pattern; the stimulating patterns are those patterns a, such that for many closed patterns b, adding the attributes of a to b reduces the support in the negative class much more than in the positive class. We present methods for comparing and attributing stimulation of closed patterns. We discuss the complexity of enumerating stimulating patterns. We discuss in particular the discovery of highly stimulating patterns and the discovery of patterns which capture contrasts. We extract these two types of stimulating patterns from UCI machine learning databases.