Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Propagating imprecise probabilities in Bayesian networks
Artificial Intelligence
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
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining interesting knowledge using DM-II
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Bucket elimination: a unifying framework for reasoning
Artificial Intelligence
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Small is beautiful: discovering the minimal set of unexpected patterns
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Empirical bayes screening for multi-item associations
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Machine Learning
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Discovery of Surprising Exception Rules Based on Intensity of Implication
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Local and Global Methods in Data Mining: Basic Techniques and Open Problems
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Pruning Redundant Association Rules Using Maximum Entropy Principle
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Bayesian Error-Bars for Belief Net Inference
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Interestingness of frequent itemsets using Bayesian networks as background knowledge
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A Bayesian network framework for reject inference
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Evaluation of rule interestingness measures with a clinical dataset on hepatitis
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Fast discovery of unexpected patterns in data, relative to a Bayesian network
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Active learning for structure in Bayesian networks
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Causal discovery from a mixture of experimental and observational data
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Evaluating the correlation between objective rule interestingness measures and real human interest
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Learning causal bayesian networks from observations and experiments: a decision theoretic approach
MDAI'06 Proceedings of the Third international conference on Modeling Decisions for Artificial Intelligence
Using interesting sequences to interactively build Hidden Markov Models
Data Mining and Knowledge Discovery
Domain-driven KDD for mining functionally novel rules and linking disjoint medical hypotheses
Knowledge-Based Systems
Intelligent fault inference for rotating flexible rotors using Bayesian belief network
Expert Systems with Applications: An International Journal
Formal and computational properties of the confidence boost of association rules
ACM Transactions on Knowledge Discovery from Data (TKDD)
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We study a discovery framework in which background knowledge on variables and their relations within a discourse area is available in the form of a graphical model. Starting from an initial, hand-crafted or possibly empty graphical model, the network evolves in an interactive process of discovery. We focus on the central step of this process: given a graphical model and a database, we address the problem of finding the most interesting attribute sets. We formalize the concept of interestingness of attribute sets as the divergence between their behavior as observed in the data, and the behavior that can be explained given the current model. We derive an exact algorithm that finds all attribute sets whose interestingness exceeds a given threshold. We then consider the case of a very large network that renders exact inference unfeasible, and a very large database or data stream. We devise an algorithm that efficiently finds the most interesting attribute sets with prescribed approximation bound and confidence probability, even for very large networks and infinite streams. We study the scalability of the methods in controlled experiments; a case-study sheds light on the practical usefulness of the approach.