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.
1. Chapter 1 An introduction to data mining in social networks - p. 1 2. Chapter 2 Performance tuning of Android applications using clustering and optimization heuristics - p. 27 3. Chapter 3 Sentiment analysis of social media data evolved from COVID-19 cases – Maharashtra - p. 51 4. Chapter 4 COVID-19 outbreak analysis and prediction using statistical learning - p. 67 5. Chapter 5 Verbal sentiment analysis and detection using recurrent neural network - p. 85 6. Chapter 6 A machine learning approach to aid paralysis patients using EMG signals - p. 107 7. Chapter 7 Influence of traveling on social behavior - p. 127 8. Chapter 8 A study on behavior analysis in social network - p. 147 9. Chapter 9 Recent trends in recommendation systems and sentiment analysis - p. 163 10. Chapter 10 Data visualization: existing tools and techniques - p. 177 11. Chapter 11 An intelligent agent to mine for frequent patterns in uncertain graphs - p. 219 12. Chapter 12 Mining challenges in large-scale IoT data framework – a machine learning perspective - p. 239 13. Chapter 13 Conclusion - p. 261 14. Index - p. 265
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.
1. Chapter 1 An introduction to data mining in social networks - p. 1 2. Chapter 2 Performance tuning of Android applications using clustering and optimization heuristics - p. 27 3. Chapter 3 Sentiment analysis of social media data evolved from COVID-19 cases – Maharashtra - p. 51 4. Chapter 4 COVID-19 outbreak analysis and prediction using statistical learning - p. 67 5. Chapter 5 Verbal sentiment analysis and detection using recurrent neural network - p. 85 6. Chapter 6 A machine learning approach to aid paralysis patients using EMG signals - p. 107 7. Chapter 7 Influence of traveling on social behavior - p. 127 8. Chapter 8 A study on behavior analysis in social network - p. 147 9. Chapter 9 Recent trends in recommendation systems and sentiment analysis - p. 163 10. Chapter 10 Data visualization: existing tools and techniques - p. 177 11. Chapter 11 An intelligent agent to mine for frequent patterns in uncertain graphs - p. 219 12. Chapter 12 Mining challenges in large-scale IoT data framework – a machine learning perspective - p. 239 13. Chapter 13 Conclusion - p. 261 14. Index - p. 265
