10 papers analyzed
These studies suggest that CRISP-DM is a widely used framework in data mining and data science projects, but it requires enhancements in deployment, flexibility for exploratory projects, and better support for project phases and team coordination.
CRISP-DM (CRoss-Industry Standard Process for Data Mining) is a widely adopted methodology for data mining projects, providing a structured approach to data analysis. Despite being over two decades old, it remains a cornerstone in the field, though it faces challenges and adaptations in the evolving landscape of data science.
Continued Relevance and Adaptation:
Challenges and Gaps:
Extensions and Enhancements:
Best Practices and Methodologies:
CRISP-DM continues to be a foundational methodology in data mining, valued for its structured approach. However, it faces challenges in deployment, task prioritization, and collaboration. Extensions like DMME and QM-CRISP-DM, as well as adaptations for agile and group-aware processes, enhance its applicability in modern data science projects. Integrating best practices and coordination frameworks can further address its limitations, ensuring its relevance in the evolving landscape of data analytics.
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