Résumé:
This Master’s thesis presents a comparative study of four popular free data
mining tools: RapidMiner, Weka, KANIME, and Orange. The study evaluates their
features, user-friendliness, performance, and community support. It examines data
preparation, clustering and classification, and performs static and dynamic studies on
various datasets. The study also assesses the integration and expansion capabilities
of each tool. The results show significant disparities in performance and usefulness,
with RapidMiner and Weka showing strong performance in managing large datasets
and complex tasks. KANIME and Orange, on the other hand, offer intuitive interfaces
and seamless connectivity with other data mining tools. This study provides valuable
insights for data scientists, researchers, and practitioners in choosing the best data
mining technology for their needs. By understanding each tool’s unique characteristics
and constraints, users can make informed choices that enhance their data analysis
processes and support evidence
