An introduction to computational learning theory
An introduction to computational learning theory
Learning Logical Definitions from Relations
Machine Learning
Machine Learning
An experimental evaluation of simplicity in rule learning
Artificial Intelligence
Constraint programming for itemset mining
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Correlated itemset mining in ROC space: a constraint programming approach
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Constraint programming for mining n-ary patterns
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Tree2: decision trees for tree structured data
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
A constraint language for declarative pattern discovery
Proceedings of the 27th Annual ACM Symposium on Applied Computing
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The pattern mining community has shifted its attention from local pattern mining to pattern set mining. The task of pattern set mining is concerned with finding a set of patterns that satisfies a set of constraints and often also scores best w.r.t. an optimisation criteria. Furthermore, while in local pattern mining the constraints are imposed at the level of individual patterns, in pattern set mining they are also concerned with the overall set of patterns. A wide variety of different pattern set mining techniques is available in literature. The key contribution of this paper is that it studies, compares and evaluates such search strategies for pattern set mining. The investigation employs concept-learning as a benchmark for pattern set mining and employs a constraint programming framework in which key components of pattern set mining are formulated and implemented. The study leads to novel insights into the strong and weak points of different pattern set mining strategies.