Practical Data Mining Techniques and Applications
Auerbach (Verlag)
978-1-032-48677-2 (ISBN)
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Data mining techniques and algorithms are extensively used to build real-world applications. A practical approach can be applied to data mining techniques to build applications. Once deployed, an application enables the developers to work on the users’ goals and mold the algorithms with respect to users’ perspectives.
Practical Data Mining Techniques and Applications focuses on various concepts related to data mining and how these techniques can be used to develop and deploy applications. The book provides a systematic composition of fundamental concepts of data mining blended with practical applications. The aim of this book is to provide access to practical data mining applications and techniques to help readers gain an understanding of data mining in practice. Readers also learn how relevant techniques and algorithms are applied to solve problems and to provide solutions to real-world applications in different domains. This book can help academicians to extend their knowledge of the field as well as their understanding of applications based on different techniques to gain greater insight. It can also help researchers with real-world applications by diving deeper into the domain. Computing science students, application developers, and business professionals may also benefit from this examination of applied data science techniques.
By highlighting an overall picture of the field, introducing various mining techniques, and focusing on different applications and research directions using these methods, this book can motivate discussions among academics, researchers, professionals, and students to exchange and develop their views regarding the dynamic field that is data mining.
Preface. Acknowledgments. Contributors. 1 Introduction to Data Mining. 2 Review of Latent Dirichlet Allocation to Understand Motivations to Share Conspiracy Theory: A Case Study of "Plandemic" during COVID 19. 3 Near Human-Level Style Transfer. 4 Semantics-Based Distributed Document Clustering. 5 Application of Machine Learning in Disease Prediction. 6 Federated Machine Learning-Based Bank Customer Churn Prediction. 7 Challenges and Avenues in the Sophisticated Health-Care System. 8 Unusual Social Media Behavior Detection Using Distributed Data Stream Mining. 9 Market Basket Analysis Using Distributed Algorithm. 10 Identification of Crime Prone-Areas Using Data Mining Techniques. 11 Smart Baby Cradle for Infant Soothing and Monitoring. 12 Word-Level Devanagari Text Recognition. 13 Wall Paint Visualizer Using Panoptic Segmentation. 14 Fashion Intelligence: An Artificial Intelligence-Based Clothing Fashion Stylist. Index.
| Erscheinungsdatum | 15.10.2024 |
|---|---|
| Zusatzinfo | 80 Line drawings, black and white; 80 Illustrations, black and white |
| Verlagsort | London |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| ISBN-10 | 1-032-48677-5 / 1032486775 |
| ISBN-13 | 978-1-032-48677-2 / 9781032486772 |
| Zustand | Neuware |
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