Handbook of research on big data clustering and machine learning /

Bibliographic Details
Corporate Author:	IGI Global Online
Other Authors:	García Márquez, Fausto Pedro (Editor)
Format:	eBook
Language:	English
Published:	Hershey, PA : Engineering Science Reference (an imprint of IGI Global), [2020]
Subjects:	Big data > Research > Handbooks, manuals, etc. Cluster analysis > Research > Handbooks, manuals, etc. Machine learning > Research > Handbooks, manuals, etc. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

Chapter 1. Big data and clustering techniques
Chapter 2. Big data analytics and models
Chapter 3. Technologies for handling big data
Chapter 4. Clustering and bayesian networks
Chapter 5. Analysis of gravitation-based optimization algorithms for clustering and classification
Chapter 6. Analytics and technology for practical forecasting
Chapter 7. Modern statistical modeling in machine learning and big data analytics: statistical models for continuous and categorical variables
Chapter 8. Enhanced logistic regression (ELR) model for big data
Chapter 9. On foundations of estimation for nonparametric regression with continuous optimization
Chapter 10. An overview of methodologies and challenges in sentiment analysis on social networks
Chapter 11. Evaluation of optimum and coherent economic-capital portfolios under complex market prospects
Chapter 12. Data-driven stochastic optimization for transportation road network design under uncertainty
Chapter 13. Examining visitors' characteristics and behaviors in tourist destinations through mobile phone users' location data
Chapter 14. Machine learning for smart tourism and retail
Chapter 15. Predictive analysis of robotic manipulators through inertial sensors and pattern recognition
Chapter 16. Call masking: a worrisome trend in Nigeria's telecommunications industry
Chapter 17. An optimized three-dimensional clustering for microarray data
Chapter 18. Identifying patterns in fresh produce purchases: the application of machine learning techniques
Chapter 19. Urban spatial data computing: integration of GIS and GPS towards location-based recommendations.