Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Frequency Estimation of Internet Packet Streams with Limited Space
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
A simple algorithm for finding frequent elements in streams and bags
ACM Transactions on Database Systems (TODS)
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
Identifying frequent items in sliding windows over on-line packet streams
Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement
An Algorithm for In-Core Frequent Itemset Mining on Streaming Data
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Finding Maximal Frequent Itemsets over Online Data Streams Adaptively
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
A simpler and more efficient deterministic scheme for finding frequent items over sliding windows
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Mining top-K frequent itemsets from data streams
Data Mining and Knowledge Discovery
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Incorporating prediction models in the SelfLet framework: a plugin approach
Proceedings of the Fourth International ICST Conference on Performance Evaluation Methodologies and Tools
A new algorithm for mining global frequent itemsets in a stream
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
Mining top-k frequent items in a data stream with flexible sliding windows
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Speed up gradual rule mining from stream data! A B-Tree and OWA-based approach
Journal of Intelligent Information Systems
Towards a variable size sliding window model for frequent itemset mining over data streams
Computers and Industrial Engineering
A framework for summarizing and analyzing twitter feeds
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Size matters: finding the most informative set of window lengths
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Mining frequent itemsets in a stream
Information Systems
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We study the problem of finding frequent items in a continuous stream of itemsets. A new frequency measure is introduced, based on a flexible window length. For a given item, its current frequency in the stream is defined as the maximal frequency over all windows from any point in the past until the current state. We study the properties of the new measure, and propose an incremental algorithm that allows to produce the current frequency of an item immediately at any time. It is shown experimentally that the memory requirements of the algorithm are extremely small for many different realistic data distributions.