A Variable Sliding Window Algorithm Based on Concept Drift for Frequent Pattern Mining Over Data Streams*
Author:
Affiliation:
1. Nanjing University of Posts and Telecommunications,School of Computer Science,Nanjing,China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10077887/10077888/10077997.pdf?arnumber=10077997
Reference23 articles.
1. An Overview on Concept Drift Learning
2. Towards a variable size sliding window model for frequent itemset mining over data streams
3. Learning from Time-Changing Data with Adaptive Windowing
4. Mining High-Speed Data Streams[C];domingos;Proceeding of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2002
5. Data stream mining: methods and challenges for handling concept drift
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2. Dual Dynamic Proxy Hashing Network for Long-tailed Image Retrieval;Proceedings of the 31st ACM International Conference on Multimedia;2023-10-26
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