Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Moment: Maintaining Closed Frequent Itemsets over a Stream Sliding Window
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Online Mining (Recently) Maximal Frequent Itemsets over Data Streams
RIDE '05 Proceedings of the 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications
CanTree: a canonical-order tree for incremental frequent-pattern mining
Knowledge and Information Systems
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
An efficient algorithm for mining temporal high utility itemsets from data streams
Journal of Systems and Software
Conceptual modeling rules extracting for data streams
Knowledge-Based Systems
Mining frequent itemsets over data streams using efficient window sliding techniques
Expert Systems with Applications: An International Journal
A tree-based approach for frequent pattern mining from uncertain data
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
A FP-tree-based method for inverse frequent set mining
BNCOD'06 Proceedings of the 23rd British National Conference on Databases, conference on Flexible and Efficient Information Handling
Research issues in outlier detection for data streams
ACM SIGKDD Explorations Newsletter
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A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Data mining over data streams should support the flexible trade-off between processing time and mining accuracy. This should occur without a fixed granule of data mining to catch the sensitive change of its mining results as soon as possible. The continuous characteristic of streaming data necessitates the use of algorithms that require only one scan over the stream for knowledge discovery. This paper focuses on research issues concerning mining frequent itemsets in data streams and presents an efficient algorithm WSFI(Weighted Support Frequent Itemsets)-mine to mine all frequent itemsets by one scan from the data stream. WSFI-mine's novel contribution is to effectively execute frequent patterns by generating constraint candidate item sets and extended FPtree-based compact pattern representation under window sliding of the data stream. This method can be achieved effectively with less memory and lowered execution time.