An Efficient Algorithm for Computing Entropic Measures of Feature Subsets-Reference-Cited by-同舟云学术

An Efficient Algorithm for Computing Entropic Measures of Feature Subsets

Published:2019 Issue: Volume: Page:483-499
ISSN:0302-9743
Container-title:Machine Learning and Knowledge Discovery in Databases
language:
Short-container-title:

Author:

Pennerath Frédéric

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-10928-8_29

Reference14 articles.

1. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of 20th International Conference on Very Large Data Bases, pp. 487–499. Morgan Kaufmann, Santiago de Chile (1994)

2. Bayardo, R., Goethals, B., Zaki, M.J. (eds.) Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations. Brighton (2004). http://fimi.ua.ac.be/

3. Dalkilic, M.M., Roberston, E.L.: Information dependencies. In: Proceedings of the 19th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 245–253. ACM, Dallas (2000)

4. Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min. Knowl. Discov. 8(1), 53–87 (2004)

5. Heikinheimo, H., Hinkkanen, E., Mannila, H., Mielikinen, T., Seppnen, J.K.: Finding low-entropy sets and trees from binary data. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, p. 350. ACM, San Jose (2007)

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. PARROT: pattern-based correlation exploitation in big partitioned data series;The VLDB Journal;2022-10-13

2. Discovering Functional Dependencies from Mixed-Type Data;Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining;2020-08-20