A Domain Knowledge-Driven Framework for Multi-Criteria Optimization-Based Data Mining Methods

In recent years, multi-criteria optimization (MCO) community has made noticeable progress in the area of data mining and knowledge discovery. While most research effort is devoted to developing models and algorithms to "mine" data, not enough attention has been paid to the "knowledge discovery" aspect. Real-world data mining problems are complex and require close collaboration between data miners and domain experts. This paper analyzes the characteristics of MCO methods and proposes a framework that supports the integration of domain knowledge, business constraints and expectations, and data mining expertise. The aim of the framework is to turn the results of MCO-based data mining methods into actionable knowledge that can be applied to real-world problems.