Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Fast discovery of association rules
Advances in knowledge discovery and data mining
A condensed representation to find frequent patterns
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
Proceedings of the 17th International Conference on Data Engineering
Concise Representation of Frequent Patterns Based on Disjunction-Free Generators
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Why to Apply Generalized Disjunction-Free Generators Representation of Frequent Patterns?
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Concise Representation of Frequent Patterns Based on Generalized Disjunction-Free Generators
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
DBC: a condensed representation of frequent patterns for efficient mining
Information Systems
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In this paper, first we introduce frequent few-overlapped monotone DNF formulas under the minimum supportσ, the minimum term supportτ and the maximum overlapλ. We say that a monotone DNF formula is frequent if the support of it is greater than σ and the support of each term (or itemset) in it is greater than τ, and few-overlapped if the overlap of it is less than λ and λ τ.Then, we design the algorithm ffo_dnf to extract them. The algorithm ffo_dnf first enumerates all of the maximal frequent itemsets under τ, and secondly connects the extracted itemsets by a disjunction ∨ until satisfying σ and λ. The first step of ffo_dnf, called a depth-first pruning, follows from the property that every pair of itemsets in a few-overlapped monotone DNF formula is incomparable under a subset relation. Furthermore, we show that the extracted formulas by ffo_dnf are representative.Finally, we apply the algorithm ffo_dnf to bacterial culture data.