Finding Frequent Items in Data Streams
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
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
Online Algorithms for Mining Semi-structured Data Stream
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Finding recent frequent itemsets adaptively over online data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
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
An Algorithm for In-Core Frequent Itemset Mining on Streaming Data
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
CFI-Stream: mining closed frequent itemsets in data streams
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A New Algorithm for Maintaining Closed Frequent Itemsets in Data Streams by Incremental Updates
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Mining maximal frequent itemsets from data streams
Journal of Information Science
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
Finding hierarchical heavy hitters in data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Mining closed itemsets in data stream using formal concept analysis
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
Expert Systems with Applications: An International Journal
A sliding window based algorithm for frequent closed itemset mining over data streams
Journal of Systems and Software
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The frequent closed itemsets determine exactly the complete set of frequent itemsets and are usually much smaller than the later. However, mining frequent closed itemsets from a landmark window over data streams is a challenging problem. To solve the problem, this paper presents a novel algorithm (called FP-CDS) that can capture all frequent closed itemsets and a new storage structure (called FP-CDS tree) that can be dynamically adjusted to reflect the evolution of itemsets' frequencies over time. A landmark window is divided into several basic windows and these basic windows are used as updating units. Potential frequent closed itemsets in each basic window are mined and stored in FP-CDS tree based on some proposed strategies. Extensive experiments are conducted to validate the proposed method.