Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
A new framework for itemset generation
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
ACM Transactions on Database Systems (TODS)
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Beyond Independence: Probabilistic Models for Query Approximation on Binary Transaction Data
IEEE Transactions on Knowledge and Data Engineering
Interestingness of frequent itemsets using Bayesian networks as background knowledge
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Finding low-entropy sets and trees from binary data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Maximum entropy based significance of itemsets
Knowledge and Information Systems
Finding Good Itemsets by Packing Data
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Tell me something I don't know: randomization strategies for iterative data mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Computational complexity of queries based on itemsets
Information Processing Letters
Tell me what i need to know: succinctly summarizing data with itemsets
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-document summarization exploiting frequent itemsets
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Summarizing data succinctly with the most informative itemsets
ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on the Best of SIGKDD 2011
A bayesian approach for classification rule mining in quantitative databases
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Misleading Generalized Itemset discovery
Expert Systems with Applications: An International Journal
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One of the main current challenges in itemset mining is to discover a small set of high-quality itemsets. In this paper we propose a new and general approach for measuring the quality of itemsets. The method is solidly founded in Bayesian statistics and decreases monotonically, allowing for efficient discovery of all interesting itemsets. The measure is defined by connecting statistical models and collections of itemsets. This allows us to score individual itemsets with the probability of them occuring in random models built on the data. As a concrete example of this framework we use exponential models. This class of models possesses many desirable properties. Most importantly, Occam's razor in Bayesian model selection provides a defence for the pattern explosion. As general exponential models are infeasible in practice, we use decomposable models; a large sub-class for which the measure is solvable. For the actual computation of the score we sample models from the posterior distribution using an MCMC approach. Experimentation on our method demonstrates the measure works in practice and results in interpretable and insightful itemsets for both synthetic and real-world data.