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
Using association rules for product assortment decisions: a case study
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Transactions on Database Systems (TODS)
KDD-Cup 2000 organizers' report: peeling the onion
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Scalable Algorithms for Association Mining
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
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
Nestedness and segmented nestedness
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Assessing data mining results via swap randomization
ACM Transactions on Knowledge Discovery from Data (TKDD)
Banded structure in binary matrices
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Maximum entropy based significance of itemsets
Knowledge and Information Systems
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
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
Summarizing data succinctly with the most informative itemsets
ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on the Best of SIGKDD 2011
Knowledge discovery interestingness measures based on unexpectedness
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
Behavior-based clustering and analysis of interestingness measures for association rule mining
Data Mining and Knowledge Discovery
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Assessing the quality of discovered results is an important open problem in data mining. Such assessment is particularly vital when mining itemsets, since commonly many of the discovered patterns can be easily explained by background knowledge. The simplest approach to screen uninteresting patterns is to compare the observed frequency against the independence model. Since the parameters for the independence model are the column margins, we can view such screening as a way of using the column margins as background knowledge. In this paper we study techniques for more flexible approaches for infusing background knowledge. Namely, we show that we can efficiently use additional knowledge such as row margins, lazarus counts, and bounds of ones. We demonstrate that these statistics describe forms of data that occur in practice and have been studied in data mining. To infuse the information efficiently we use a maximum entropy approach. In its general setting, solving a maximum entropy model is infeasible, but we demonstrate that for our setting it can be solved in polynomial time. Experiments show that more sophisticated models fit the data better and that using more information improves the frequency prediction of itemsets.