Mining Feature Relationships in Data-Reference-Cited by-同舟云学术

Mining Feature Relationships in Data

Published:2021 Issue: Volume: Page:247-262
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
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Author:

Lensen Andrew^ORCID

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-72812-0_16

Reference30 articles.

1. Tukey, J.W.: Exploratory data analysis, vol. 2. Reading, MA (1977)

2. Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, DC, USA, pp. 207–216, May 26–28, 1993. ACM Press (1993)

3. Lecture Notes in Computer Science;G Dick,2014

4. Poli, R., Langdon, W.B., McPhee, N.F.: A Field Guide to Genetic Programming (2008). https://lulu.com. Accessed 27 Sept 2019

5. Neshatian, K., Zhang, M., Andreae, P.: A filter approach to multiple feature construction for symbolic learning classifiers using genetic programming. IEEE Trans. Evol. Comput. 16(5), 645–661 (2012)

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