Frequent Itemset Mining from Databases Including One Evidential Attribute
SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
Probabilistic frequent itemset mining in uncertain databases
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Incremental Maintenance of Frequent Itemsets in Evidential Databases
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Efficient algorithms for mining constrained frequent patterns from uncertain data
Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data
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
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
Evaluating the distance between two uncertain categorical objects
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - 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
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
A new mining approach for uncertain databases using CUFP trees
Expert Systems with Applications: An International Journal
Efficient computation of measurements of correlated patterns in uncertain data
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
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
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
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
Stream mining on univariate uncertain data
Applied Intelligence
EMU: An expectation maximization based approach for clustering uncertain data
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Since its introduction, mining of frequent patterns has been the subject of numerous studies. Generally, they fo- cus on improving algorithmic efficiency for finding frequent patterns or on extending the notion of frequent patterns to other interesting patterns. Most of these studies find pat- terns from traditional transaction databases, in which the content of each transaction--namely, items--is definitely known and precise. However, there are many real-life sit- uations in which ones are uncertain about the content of transactions. To deal with these situations, we propose a tree-based mining algorithm to efficiently find frequent pat- terns from uncertain data, where each item in the transac- tions is associated with an existential probability. Experi- mental results show the efficiency of our algorithm over its non-tree-based counterpart.