Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Efficiently Mining Approximate Models of Associations in Evolving Databases
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Finding Frequent Items in Data Streams
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
What's hot and what's not: tracking most frequent items dynamically
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Issues in data stream management
ACM SIGMOD Record
Dynamically maintaining frequent items over a data stream
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
IEEE Transactions on Pattern Analysis and Machine Intelligence
Framework and algorithms for trend analysis in massive temporal data sets
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
On the estimation of frequent itemsets for data streams: theory and experiments
Proceedings of the 14th ACM international conference on Information and knowledge management
Closeness Preference - A new interestingness measure for sequential rules mining
Knowledge-Based Systems
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When we mine information for knowledge on a whole data streams it's necessary to cope with uncertainty as only a part of the stream is available. We introduce a stastistical technique, independant from the used algorithm, for estimating the frequent itemset on a stream. This statistical support allows to maximize either the precision or the recall as choosen by the user, while it doesn't damage the other. Experiments with various association rules databases demonstrate the potential of such technique.