eBook - Data Mining and Machine Learning Applications
Available Exclusively as an eBook | Part of the AI eBook Collection | Only for Institutional Purchase | Publication Year: 2022
Description
This comprehensive text provides both theoretical foundations and practical methodologies for data mining and machine learning, highlighting their synergistic roles in extracting meaningful knowledge from large, complex datasets. It explores core algorithms, model evaluation strategies, feature selection techniques, and predictive analytics approaches, demonstrating their applications in domains such as finance, healthcare, marketing, and sensor networks. The book balances mathematical rigor with real-world examples, case studies, and experimental frameworks that illustrate how data-driven decision-making can be implemented effectively. Emphasizing scalability, interpretability, and ethical considerations, the volume serves as a key reference for graduate students, data scientists, and engineering professionals.
Table of Contents
1 Introduction to Data Mining
2 Classification and Mining Behavior of Data
3 A Comparative Overview of Hybrid Recommender Systems: Review, Challenges, and Prospects
4 Stream Mining: Introduction, Tools & Techniques and Applications
5 Data Mining Tools and Techniques: Clustering Analysis
6 Data Mining Implementation Process
7 Predictive Analytics in IT Service Management (ITSM)
8 Modified Cross-Sell Model for Telecom Service Providers Using Data Mining Techniques
9 Inductive Learning Including Decision Tree and Rule Induction Learning
10 Data Mining for Cyber-Physical Systems
11 Developing Decision Making and Risk Mitigation: Using CRISP-Data Mining
12 Human–Machine Interaction and Visual Data Mining
13 MSDTrA: A Boosting Based-Transfer Learning Approach for Class Imbalanced Skin Lesion Dataset for Melanoma Detection
14 New Algorithms and Technologies for Data Mining
15 Classification of EEG Signals for Detection of Epileptic Seizure Using Restricted Boltzmann Machine Classifier
16 An Enhanced Security of Women and Children Using Machine Learning and Data Mining Techniques
17 Conclusion and Future Direction in Data Mining and Machine Learning
Index