Value of fuzzy logic for data mining and machine learning: A case study

Author:

Mirzakhanov Vugar E.ORCID

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference95 articles.

1. Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 207–216. https://doi.org/10.1145/170035.170072.

2. Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., & Verkamo, A. I. (1996). Fast discovery of association rules. In U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, & R. Uthurusamy (Eds.), Advances in Knowledge Discovery and Data Mining (pp. 307–328). Retrieved from http://dl.acm.org/citation.cfm?id=257938.257975.

3. Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 20th VLDB Conference, 487–499. San Francisco: Morgan Kaufmann Publishers Inc.

4. Multi-objective genetic algorithms based automated clustering for fuzzy association rules mining;Alhajj;Journal of Intelligent Information Systems,2008

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