Mining Top-K constrained cross-level high-utility itemsets over data streams
Author:
Funder
the National Nature Science Foundation of China
the Ningxia Natural Science Foundation Project
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10115-023-02045-8.pdf
Reference36 articles.
1. Han M, Zhang N, Wang L, Li XJ, Cheng HD (2023) Mining closed high utility patterns with negative utility in dynamic databases. Appl Intell 53(10):11750–11767
2. Lin JC-W, Djenouri Y, Srivastava G, Yun U, Fournier-Viger P (2021) A predictive GA-based model for closed high-utility itemset mining. Appl Soft Comput 108:107422
3. Dawar S, Sharma V, Goyal V (2017) Mining top-k high-utility itemsets from a data stream under sliding window model. Appl Intell 47(4):1240–1255
4. Srikant R, Agrawal R (1997) Mining generalized association rules. Futur Gener Comput Syst 13(2–3):161–180
5. Hipp J, Myka A, Wirth R, Güntzer U (2016) A new algorithm for faster mining of generalized association rules. Proceedings of the Principles of Data Mining and Knowledge Discovery: Second European Symposium, PKDD’98 Nantes. Springer, Berlin and Heidelberg, Berlin, pp. 74–82
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