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"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"--.
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Table of Contents
- 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
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