TOPSIL-Miner: an efficient algorithm for mining top-K significant itemsets over data streams

Author:

Yang Bei,Huang Houkuan

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Hardware and Architecture,Human-Computer Interaction,Information Systems,Software

Reference24 articles.

1. Agrawal R, SriKant R (1994) Fast algorithms for mining association rules. In: Proceedings of the 20th international conference on very large databases, pp 487–499

2. Babcock B, Babu S, Datar M et al (2002) Models and issues in data streams. In: Proceedings of the 21st ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems, pp 1–16

3. Babcock B, Olston C (2003) Distributed top-K monitoring. In: Proceedings of the 2003 ACM SIGMOD international conference on management of data, pp 28–39

4. Chang JH, Lee WS (2004) A sliding window method for finding recently frequent itemsets over online data streams. J Inform Sci Eng 20(2): 753–762

5. Chang JH, Lee WS (2006) Finding recently frequent itemsets adaptively over online transactional data streams. Inform Syst 31(8): 849–869

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2. Mining top-k high-utility itemsets from a data stream under sliding window model;Applied Intelligence;2017-06-08

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4. FPS-Tree Algorithm to Find Top-k Closed Itemsets in Data Streams;Arabian Journal for Science and Engineering;2015-08-14

5. TKAP: Efficiently processing top-k query on massive data by adaptive pruning;Knowledge and Information Systems;2015-05-01

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