Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Data mining of multidimensional remotely sensed images
CIKM '93 Proceedings of the second international conference on Information and knowledge management
A probabilistic relational model and algebra
ACM Transactions on Database Systems (TODS)
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
GeoMiner: a system prototype for spatial data mining
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Data-Driven Discovery of Quantitative Rules in Relational Databases
IEEE Transactions on Knowledge and Data Engineering
Mining dynamic databases by weighting
Acta Cybernetica
Association rule mining: models and algorithms
Association rule mining: models and algorithms
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Modeling probabilistic data is one of important issues in databases due to the fact that data is often uncertainty in real-world applications. So, it is necessary to identify potentially useful patterns in probabilistic databases. Because probabilistic data in 1NF relations is redundant, previous mining techniques don't work well on probabilistic databases. For this reason, this paper proposes a new model for mining probabilistic databases. A partition is thus developed for preprocessing probabilistic data in a probabilistic databases. We evaluated the proposed technique, and the experimental results demonstrate that our approach is effective and efficient.