Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 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
Fuzzy association rules and the extended mining algorithms
Information Sciences—Informatics and Computer Science: An International Journal
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Sensor fusion and automatic vulnerability analysis
WISICT '05 Proceedings of the 4th international symposium on Information and communication technologies
A systematic approach to the assessment of fuzzy association rules
Data Mining and Knowledge Discovery
Efficient Mining of Frequent Patterns from Uncertain Data
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Mining frequent itemsets from uncertain data
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
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Frequent Itemset Mining (FIM) problem has been extensively tackled in the context of perfect data. However, real applications showed that data are often imperfect (incomplete and/or uncertain) which leads to the need of FIM algorithms that process imperfect databases. In this paper we propose a new algorithm for mining frequent itemsets from databases including exactly one evidential attribute. An evidential attribute is an attribute that could have uncertain values modelled via the evidence theory, i.e., a basic belief assignment. We introduce in this paper a variant of the structure Belief Itemset Tree (BIT) for mining frequent itemsets from evidential data and we lead some experiments that showed efficiency of our mining algorithm compared to the existing ones.