We are not able to resolve this OAI Identifier to the repository landing page. If you are the repository manager for this record, please head to the Dashboard and adjust the settings.
Institute of Informatics, Slovak Academy of Sciences
Abstract
This paper considers the problem of mining recent frequent itemsets over data streams. As the data grows without limit at a rapid rate, it is hard to track the new changes of frequent itemsets over data streams. We propose an efficient one-pass algorithm in sliding windows over data streams with an error bound guarantee. This algorithm does not need to refer to obsolete transactions when they are removed from the sliding window. It exploits a compact data structure to maintain potentially frequent itemsets so that it can output recent frequent itemsets at any time. Flexible queries for continuous transactions in the sliding window can be answered with an error bound guarantee
Is data on this page outdated, violates copyrights or anything else? Report the problem now and we will take corresponding actions after reviewing your request.