About this ebook
"Applied Data Mining with Weka"
"Applied Data Mining with Weka" is a comprehensive and authoritative guide designed for professionals and advanced students seeking a rigorous yet practical exploration of modern data mining techniques through the versatile Weka platform. The book lays a solid foundation with an in-depth discussion of data mining principles, essential paradigms, and the integration of mining tasks within larger data science workflows. Readers are systematically introduced to the taxonomy of core data mining activities, challenges inherent to data-driven discovery, and the metrics underpinning quality, interpretability, and reproducibility.
Diving deeply into Weka, the book details its modular architecture, diverse user interfaces, data connectivity, and the rapidly evolving ecosystem enriched by community-driven extensions. Each stage of the data mining process is carefully examined, from robust data preparation and feature engineering to state-of-the-art supervised and unsupervised algorithms, including classification, regression, clustering, association analysis, and dimensionality reduction. The narrative extends to specialized domains such as text mining, sequence analysis, anomaly detection, ensemble learning, and real-time mining, highlighting practical solutions for both traditional and emerging analytical challenges.
Complemented by hands-on project walkthroughs—covering customer segmentation, sentiment analysis, fraud detection, and time series forecasting—this work not only elucidates programming and automation via Weka's Java APIs but also addresses ethical considerations, model governance, and the operationalization of data mining pipelines in production environments. With a forward-looking survey of trends like AutoML and federated learning, "Applied Data Mining with Weka" is an indispensable reference for leveraging Weka’s capabilities to build transparent, reproducible, and impactful analytical solutions.
"Applied Data Mining with Weka" is a comprehensive and authoritative guide designed for professionals and advanced students seeking a rigorous yet practical exploration of modern data mining techniques through the versatile Weka platform. The book lays a solid foundation with an in-depth discussion of data mining principles, essential paradigms, and the integration of mining tasks within larger data science workflows. Readers are systematically introduced to the taxonomy of core data mining activities, challenges inherent to data-driven discovery, and the metrics underpinning quality, interpretability, and reproducibility.
Diving deeply into Weka, the book details its modular architecture, diverse user interfaces, data connectivity, and the rapidly evolving ecosystem enriched by community-driven extensions. Each stage of the data mining process is carefully examined, from robust data preparation and feature engineering to state-of-the-art supervised and unsupervised algorithms, including classification, regression, clustering, association analysis, and dimensionality reduction. The narrative extends to specialized domains such as text mining, sequence analysis, anomaly detection, ensemble learning, and real-time mining, highlighting practical solutions for both traditional and emerging analytical challenges.
Complemented by hands-on project walkthroughs—covering customer segmentation, sentiment analysis, fraud detection, and time series forecasting—this work not only elucidates programming and automation via Weka's Java APIs but also addresses ethical considerations, model governance, and the operationalization of data mining pipelines in production environments. With a forward-looking survey of trends like AutoML and federated learning, "Applied Data Mining with Weka" is an indispensable reference for leveraging Weka’s capabilities to build transparent, reproducible, and impactful analytical solutions.
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