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
Optimization of constrained frequent set queries with 2-variable constraints
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Models and algorithms for probabilistic and Bayesian logic
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
The implication problem for measure-based constraints
Information Systems
Itemset frequency satisfiability: Complexity and axiomatization
Theoretical Computer Science
An anytime deduction algorithm for the probabilistic logic and entailment problems
International Journal of Approximate Reasoning
| Hi-index | 5.23 |
Mining association rules is very popular in the data mining community. Most algorithms designed for finding association rules start with searching for frequent itemsets. Typically, in these algorithms, counting phases and pruning phases are interleaved. In the counting phase, partial information about the frequencies of selected itemsets is gathered. In the pruning phase as much as possible of the search space is pruned, based on the counting information. We introduce frequent set expressions to represent (possible partial) information acquired in the counting phase. A frequent set expression is a pair containing an itemset and a fraction that is a lower bound on the actual frequency of the itemset. A system of frequent sets is a collection of such pairs. We give an axiomatization for those systems that are complete in the sense that they explicitly contain all information they logically imply. Every system of frequent sets has a unique completion that actually represents all knowledge that can be derived. We also study sparse systems, in which not for every frequent set an expression is given. Furthermore, we explore the links with probabilistic logics.