"This book examines the relationship between the analytic principles of clustering and machine learning to big data. It also explores the connection between engineering/technology and the organizational, administrative, and planning abilities of management"--.

• Title Page
• Copyright Page
• Book Series
• List of Contributors
• Table of Contents
• Detailed Table of Contents
• Preface
• 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
• 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
• Chapter 17: An Optimized Three-Dimensional Clustering for Microarray Data
• Chapter 18: Identifying Patterns in Fresh Produce Purchases
• Chapter 19: Urban Spatial Data Computing
• Compilation of References
• About the Contributors
• Index