Alice  and the Caterpillar: A more descriptive null model for assessing data mining results

Author:

Preti Giulia,De Francisci Morales Gianmarco,Riondato Matteo

Funder

National Science Foundation

Publisher

Springer Science and Business Media LLC

Subject

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

Reference67 articles.

1. Abuissa M, Lee A, Riondato M (2023) ROhAN: Row-order agnostic null models for statistically-sound knowledge discovery. Data Min Knowl Discov. https://doi.org/10.1007/s10618-023-00938-4

2. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th international conference on very large data bases. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, VLDB ’94, pp 487–499

3. Akoglu L, Faloutsos C (2009) Rtg: A recursive realistic graph generator using random typing. In: Joint European conference on machine learning and knowledge discovery in databases, Springer, pp 13–28

4. Aksoy SG, Kolda TG, Pinar A (2017) Measuring and modeling bipartite graphs with community structure. J Complex Netw 5(4):581–603

5. Amanatidis G, Green B, Mihail M (2015) Graphic realizations of joint-degree matrices. arXiv preprint arXiv:1509.07076

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