H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases
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
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Fast vertical mining using diffsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Frequent pattern mining with uncertain data
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent itemsets from uncertain data
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
A decremental approach for mining frequent itemsets from uncertain data
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
A tree-based approach for frequent pattern mining from uncertain data
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Equivalence class transformation based mining of frequent itemsets from uncertain data
Proceedings of the 2011 ACM Symposium on Applied Computing
Frequent pattern mining from time-fading streams of uncertain data
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
Mining frequent patterns from univariate uncertain data
Data & Knowledge Engineering
Mining fault-tolerant item sets using subset size occurrence distributions
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Fast tree-based mining of frequent itemsets from uncertain data
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
Mining frequent itemsets over uncertain databases
Proceedings of the VLDB Endowment
Mining probabilistic datasets vertically
Proceedings of the 16th International Database Engineering & Applications Sysmposium
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
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
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Mining frequent itemsets from transactional datasets is a well known problem with good algorithmic solutions. In the case of uncertain data, however, several new techniques have been proposed. Unfortunately, these proposals often suffer when a lot of items occur with many different probabilities. Here we propose an approach based on sampling by instantiating “possible worlds” of the uncertain data, on which we subsequently run optimized frequent itemset mining algorithms. As such we gain efficiency at a surprisingly low loss in accuracy. These is confirmed by a statistical and an empirical evaluation on real and synthetic data.