Artificial Intelligence
Journal of Complexity
A logic for reasoning about probabilities
Information and Computation - Selections from 1988 IEEE symposium on logic in computer science
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Anytime deduction for probabilistic logic
Artificial Intelligence
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
Probabilistic logic programming with conditional constraints
ACM Transactions on Computational Logic (TOCL)
Mining frequent patterns with counting inference
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Foundations of Databases: The Logical Level
Foundations of Databases: The Logical Level
Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries
Data Mining and Knowledge Discovery
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Axiomatization of frequent itemsets
Theoretical Computer Science
Mining All Non-derivable Frequent Itemsets
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
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
Beyond Independence: Probabilistic Models for Query Approximation on Binary Transaction Data
IEEE Transactions on Knowledge and Data Engineering
Approximating a collection of frequent sets
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Computational complexity of itemset frequency satisfiability
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Summarizing itemset patterns: a profile-based approach
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Approximate Inverse Frequent Itemset Mining: Privacy, Complexity, and Approximation
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Blocking Anonymity Threats Raised by Frequent Itemset Mining
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Computational complexity of queries based on itemsets
Information Processing Letters
Data Mining and Knowledge Discovery
Models and algorithms for probabilistic and Bayesian logic
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Ask a better question, get a better answer a new approach to private data analysis
ICDT'07 Proceedings of the 11th international conference on Database Theory
A further study on inverse frequent set mining
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
A survey on condensed representations for frequent sets
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Mining conjunctive sequential patterns
Data Mining and Knowledge Discovery
Maximum entropy models and subjective interestingness: an application to tiles in binary databases
Data Mining and Knowledge Discovery
Solving inverse frequent itemset mining with infrequency constraints via large-scale linear programs
ACM Transactions on Knowledge Discovery from Data (TKDD)
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Computing frequent itemsets is one of the most prominent problems in data mining. We study the following related problem, called FREQSAT, in depth: given some itemset-interval pairs, does there exist a database such that for every pair the frequency of the itemset falls into the interval? This problem is shown to be NP-complete. The problem is then further extended to include arbitrary Boolean expressions over items and conditional frequency expressions in the form of association rules. We also show that, unless P equals NP, the related function problem-find the best interval for an itemset under some frequency constraints-cannot be approximated efficiently. Furthermore, it is shown that FREQSAT is recursively axiomatizable, but that there cannot exist an axiomatization of finite arity.