Advanced Multi-industry Applications of Big Data Clustering and Machine Learning

✍ Scribed by Fausto Pedro Garcia Marquez (editor)

• Publisher: Engineering Science Reference
• Year: 2020
• Tongue: English
• Leaves: 500
• Series: Advances in Data Mining and Database Management
• Category: Library

✦ Synopsis

As organizations continue to develop, there is an increasing need for technological methods that can keep up with the rising amount of data and information that is being generated. Machine learning is a tool that has become powerful due to its ability to analyze large amounts of data quickly. Machine learning is one of many technological advancements that is being implemented into a multitude of specialized fields. An extensive study on the execution of these advancements within professional industries is necessary. Advanced Multi-Industry Applications of Big Data Clustering and Machine Learning is an essential reference source that synthesizes the analytic principles of clustering and machine learning to big data and provides an interface between the main disciplines of engineering/technology and the organizational, administrative, and planning abilities of management. Featuring research on topics such as project management, contextual data modeling, and business information systems, this book is ideally designed for engineers, economists, finance officers, marketers, decision makers, business professionals, industry practitioners, academicians, students, and researchers seeking coverage on the implementation of big data and machine learning within specific professional fields.

✦ 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