Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining frequent itemsets from uncertain data
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Probabilistic frequent itemset mining in uncertain databases
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient algorithms for mining constrained frequent patterns from uncertain data
Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data
Mining uncertain data for constrained frequent sets
IDEAS '09 Proceedings of the 2009 International Database Engineering & Applications Symposium
Efficient algorithms for the mining of constrained frequent patterns from uncertain data
ACM SIGKDD Explorations Newsletter
Direct mining of discriminative patterns for classifying uncertain data
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Associative classifier for uncertain data
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Similarity search and mining in uncertain databases
Proceedings of the VLDB Endowment
A sampling based algorithm for finding association rules from uncertain data
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
Mining sequential patterns from probabilistic databases
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
A practice probability frequent pattern mining method over transactional uncertain data streams
UIC'11 Proceedings of the 8th international conference on Ubiquitous intelligence and computing
Mining frequent patterns from univariate uncertain data
Data & Knowledge Engineering
Efficient pattern mining of uncertain data with sampling
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Fast approximation of probabilistic frequent closed itemsets
Proceedings of the 50th Annual Southeast Regional Conference
Incremental update on probabilistic frequent itemsets in uncertain databases
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
Mining frequent itemsets over uncertain databases
Proceedings of the VLDB Endowment
Probabilistic frequent pattern growth for itemset mining in uncertain databases
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Mining frequent subgraphs over uncertain graph databases under probabilistic semantics
The VLDB Journal — The International Journal on Very Large Data Bases
Constrained frequent pattern mining on univariate uncertain data
Journal of Systems and Software
FARP: Mining fuzzy association rules from a probabilistic quantitative database
Information Sciences: an International Journal
Discovering frequent itemsets on uncertain data: a systematic review
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
Stream mining on univariate uncertain data
Applied Intelligence
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We study the problem of mining frequent itemsets from uncertain data under a probabilistic model. We consider transactions whose items are associated with existential probabilities. A decremental pruning (DP) technique, which exploits the statistical properties of items' existential probabilities, is proposed. Experimental results show that DP can achieve significant computational cost savings compared with existing approaches, such as U-Apriori and LGS-Trimming. Also, unlike LGS-Trimming, DP does not require a user-specified trimming threshold and its performance is relatively insensitive to the population of low-probability items in the dataset.