Ensuring the Robustness and Reliability of Data-Driven Knowledge Discovery Models in Production and Manufacturing

The Cross-Industry Standard Process for Data Mining (CRISP-DM) is a widely accepted framework in production and manufacturing. This data-driven knowledge discovery provides an orderly partition of the often complex data mining processes to ensure practical implementation analytics machine learning models. However, application robust industry-specific models faces multiple data- model development-related issues. These issues need be carefully addressed by allowing flexible, customized framework. For this reason, extensions CRISP-DM are needed. In paper, we provide detailed review summarize into novel call Generalized Science (GCRISP-DS). designed allow dynamic interactions between different phases adequately address model-related achieving robustness. Furthermore, it emphasizes also business understanding interdependencies with developed quality fulfilling higher objectives. Overall, such customizable GCRISP-DS enhancement improvements reusability minimizing robustness-issues.