Advanced Data Mining Tools and Methods for Social Computing
- Length: 292 pages
- Edition: 1
- Language: English
- Publisher: Academic Press
- Publication Date: 2022-02-03
- ISBN-10: 0323857086
- ISBN-13: 9780323857086
- Sales Rank: #0 (See Top 100 Books)
Advanced Data Mining Tools and Methods for Social Computing explores advances in the latest data mining tools, methods, algorithms and the architectures being developed specifically for social computing and social network analysis. The book reviews major emerging trends in technology that are supporting current advancements in social networks, including data mining techniques and tools. It also aims to highlight the advancement of conventional approaches in the field of social networking. Chapter coverage includes reviews of novel techniques and state-of-the-art advances in the area of data mining, machine learning, soft computing techniques, and their applications in the field of social network analysis.
Copyright Dedication List of contributors Preface Chapter 1: An introduction to data mining in social networks Abstract 1.1. Introduction 1.2. Data mining concepts 1.3. Social computing 1.4. Clustering and classification References Chapter 2: Performance tuning of Android applications using clustering and optimization heuristics 2.1. Introduction 2.2. Related work 2.3. Research methodology 2.4. Subject applications 2.5. Implementation phase 1 – clustering and knapsack solvers 2.6. Implementation phase 2 – Ant colony optimization 2.7. Results and findings 2.8. Threats to validity 2.9. Conclusion Chapter 3: Sentiment analysis of social media data evolved from COVID-19 cases – Maharashtra 3.1. Introduction 3.2. Literature review 3.3. Proposed design 3.4. Analysis and predictions 3.5. Conclusion 3.6. Acknowledgment Chapter 4: COVID-19 outbreak analysis and prediction using statistical learning 4.1. Introduction 4.2. Related literature 4.3. Proposed model 4.4. Prophet 4.5. Results and discussion 4.6. Conclusion Chapter 5: Verbal sentiment analysis and detection using recurrent neural network 5.1. Introduction 5.2. Sources for sentiment detection 5.3. Literature survey 5.4. Machine learning techniques for sentiment analysis 5.5. Proposed method 5.6. Results and discussion 5.7. Conclusions Chapter 6: A machine learning approach to aid paralysis patients using EMG signals 6.1. Introduction 6.2. Associated works 6.3. System model 6.4. Simulation and results 6.5. Conclusion Chapter 7: Influence of traveling on social behavior 7.1. Introduction 7.2. Related work 7.3. Importance of social networking in real life 7.4. Dynamics of traveling 7.5. Dynamics-based social behavior analysis 7.6. Recognition of human social behavior using machine learning techniques 7.7. Conclusion Chapter 8: A study on behavior analysis in social network 8.1. Introduction 8.2. Basic concepts of behavior analysis in social networks 8.3. Uses of behavior analysis in social networks 8.4. Future direction 8.5. Conclusion Chapter 9: Recent trends in recommendation systems and sentiment analysis 9.1. Introduction 9.2. Basic terms and concepts of sentiment analysis and recommendation systems 9.3. Overview of sentiment analysis approaches in recommendation systems 9.4. Recent developments (related work) 9.5. Challenges 9.6. Future direction 9.7. Conclusion Chapter 10: Data visualization: existing tools and techniques 10.1. Introduction 10.2. Prior research works on data visualization issues 10.3. Challenges during visualization of innumerable data 10.4. Existing data visualization tools and techniques with key characteristics 10.5. Conclusion Chapter 11: An intelligent agent to mine for frequent patterns in uncertain graphs 11.1. Introduction 11.2. Related work 11.3. Mining graphs and uncertainty 11.4. Methodology 11.5. Implementation 11.6. Conclusion 11.7. Future directions Chapter 12: Mining challenges in large-scale IoT data framework – a machine learning perspective 12.1. Introduction 12.2. Review of literature 12.3. Proposed work 12.4. Application framework 12.5. H2O work flow environment 12.6. Experimental results 12.7. Discussion and conclusion Chapter 13: Conclusion Index
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