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
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
Data Mining and Knowledge Discovery
Finding Interesting Associations without Support Pruning
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Graphical Models in Applied Multivariate Statistics
Graphical Models in Applied Multivariate Statistics
On inclusion-driven learning of bayesian networks
The Journal of Machine Learning Research
A data driven ensemble classifier for credit scoring analysis
Expert Systems with Applications: An International Journal
A concise representation of association rules using minimal predictive rules
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Data mining in inductive databases
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Journal of Medical Systems
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We present the Mambo algorithm for the discovery of association rules. Mambo is driven by conditional independence relations between the variables instead of the minimum support restrictions of algorithms like Apriori. We argue that making use of conditional independencies is an intuitively appealing way to restrict the set of association rules considered. Since we only have a finite sample from the probability distribution of interest, we have to deal with uncertainty concerning the conditional independencies present. Bayesian methods are used to quantify this uncertainty, and the posterior probabilities of conditional independence relations are estimated with the Markov Chain Monte Carlo technique. We analyse an insurance data set with Mambo and illustrate the differences in results compared to Apriori.