Resource-oriented Approximation for Frequent Itemset Mining from Bursty Data Streams
Summary: Resource-oriented approximation for frequent itemset mining on bursty data streams; memory bounded to O(k) and per-transaction time O(kL), avoiding exponential blowup. Output error is bounded with possible false negatives only under certain conditions; dynamic stream reduction and experiments show it outperforms prior space-limited FIM-DS methods. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Yoshitaka Yamamoto
- 2. Koji Iwanuma
- 3. Shoshi Fukuda
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| 166 | Approximate Frequency Counts over Data Streams | 2002 | VLDB | 0.00039361552 |
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