How To Use Weka
Download How To Use Weka PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get How To Use Weka book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Data Mining
Data Mining, Second Edition, describes data mining techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights of this new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; and much more. This text is designed for information systems practitioners, programmers, consultants, developers, information technology managers, specification writers as well as professors and students of graduate-level data mining and machine learning courses. - Algorithmic methods at the heart of successful data mining—including tried and true techniques as well as leading edge methods - Performance improvement techniques that work by transforming the input or output
Applied Data Mining with Weka
"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.
Data Science Concepts and Techniques with Applications
This textbook comprehensively covers both fundamental and advanced topics related to data science. Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. The chapters of this book are organized into three parts: The first part (chapters 1 to 3) is a general introduction to data science. Starting from the basic concepts, the book will highlight the types of data, its use, its importance and issues that are normally faced in data analytics, followed by presentation of a wide range of applications and widely used techniques in data science. The second part, which has been updated and considerably extended compared to the first edition, is devoted to various techniques and tools applied in data science. Its chapters 4 to 10 detail data pre-processing, classification, clustering, text mining, deep learning, frequent pattern mining, and regression analysis. Eventually, the third part (chapters 11 and 12) present a brief introduction to Python and R, the two main data science programming languages, and shows in a completely new chapter practical data science in the WEKA (Waikato Environment for Knowledge Analysis), an open-source tool for performing different machine learning and data mining tasks. An appendix explaining the basic mathematical concepts of data science completes the book. This textbook is suitable for advanced undergraduate and graduate students as well as for industrial practitioners who carry out research in data science. They both will not only benefit from the comprehensive presentation of important topics, but also from the many application examples and the comprehensive list of further readings, which point to additional publications providing more in-depth research results or provide sources for a more detailed description of related topics. "This book delivers a systematic, carefully thoughtful material on Data Science." from the Foreword by Witold Pedrycz, U Alberta, Canada.