Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
A simple algorithm for finding frequent elements in streams and bags
ACM Transactions on Database Systems (TODS)
Dynamically maintaining frequent items over a data stream
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Moment: Maintaining Closed Frequent Itemsets over a Stream Sliding Window
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Multi-dimensional regression analysis of time-series data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A regression-based temporal pattern mining scheme for data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Efficient Single-Pass Mining of Weighted Interesting Patterns
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Handling Dynamic Weights in Weighted Frequent Pattern Mining
IEICE - Transactions on Information and Systems
Speed up gradual rule mining from stream data! A B-Tree and OWA-based approach
Journal of Intelligent Information Systems
A dynamic layout of sliding window for frequent itemset mining over data streams
Journal of Systems and Software
Single-pass incremental and interactive mining for weighted frequent patterns
Expert Systems with Applications: An International Journal
Interactive mining of high utility patterns over data streams
Expert Systems with Applications: An International Journal
Incremental Algorithm for Discovering Frequent Subsequences in Multiple Data Streams
International Journal of Data Warehousing and Mining
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Mining frequent patterns on streaming data is a new challenging problem for the data mining community since data arrives sequentially in the form of continuous rapid streams. In this paper we propose a new approach for mining itemsets. Our approach has the following advantages: an efficient representation of items and a novel data structure to maintain frequent patterns coupled with a fast pruning strategy. At any time, users can issue requests for frequent itemsets over an arbitrary time interval. Furthermore our approach produces an approximate answer with an assurance that it will not bypass user-defined frequency and temporal thresholds. Finally the proposed method is analyzed by a series of experiments on different datasets.