Data mining methods for knowledge discovery in multi-objective optimization: Part B - New developments and applications
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- 作者：Sunith Bandaru ; a ; sunith.bandaru@his.se" class="auth_mail" title="E-mail the corresponding author ; sunith.bandaru@gmail.com" class="auth_mail" title="E-mail the corresponding author ; Amos H.C. Nga ; amos.ng@his.se" class="auth_mail" title="E-mail the corresponding author ; Kalyanmoy Debb ; kdeb@egr.msu.edu" class="auth_mail" title="E-mail the corresponding author
- 关键词：Data mining ; Knowledge discovery ; Multi-objective optimization ; Discrete variables ; Production systems ; Flexible pattern mining
- 刊名：Expert Systems with Applications
- 出版年：2017
- 出版时间：15 March 2017
- 年：2017
- 卷：70
- 期：Complete
- 页码：119-138
- 全文大小：11741 K
文摘
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Four methods are developed for data mining discrete multi-objective optimization datasets.
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Two of the methods are unsupervised, one is supervised and the other is hybrid.
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Knowledge is represented as patterns in one method, and as rules in other methods.
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Methods are applied to three real-world production system optimization problems.
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Extracted knowledge is compared across methods and provides new insights.