Data mining methods for knowledge discovery in multi-objective optimization: Part B - New developments and applications
作者：
Highlights：
• Four methods are developed for data mining discrete multi-objective optimization datasets.
• Two of the methods are unsupervised, one is supervised and the other is hybrid.
• Knowledge is represented as patterns in one method, and as rules in other methods.
• Methods are applied to three real-world production system optimization problems.
• Extracted knowledge is compared across methods and provides new insights.
摘要
•Four methods are developed for data mining discrete multi-objective optimization datasets.•Two of the methods are unsupervised, one is supervised and the other is hybrid.•Knowledge is represented as patterns in one method, and as rules in other methods.•Methods are applied to three real-world production system optimization problems.•Extracted knowledge is compared across methods and provides new insights.
论文关键词：Data mining,Knowledge discovery,Multi-objective optimization,Discrete variables,Production systems,Flexible pattern mining
论文评审过程：Received 17 December 2015, Revised 13 August 2016, Accepted 10 October 2016, Available online 15 October 2016, Version of Record 21 November 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2016.10.016
