Pedro F., Marquez G. Handbook of Research on Big Data Clustering and Machine Learning

USA: IGI Global, 2020. — 500 p. — (Advances in Data Mining and Database Management). — ISBN: 9781799801061.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.The Handbook of Research on 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.Big Data and Clustering Techniques
Big Data Analytics and Models
Technologies for Handling Big Data
Clustering and Bayesian Networks
Analysis of Gravitation-Based Optimization Algorithms for Clustering and Classification
Analytics and Technology for Practical Forecasting
Modern Statistical Modeling in Machine Learning and Big Data Analytics: Statistical Models for Continuous and Categorical Variables
Enhanced Logistic Regression (ELR) Model for Big Data
On Foundations of Estimation for Nonparametric Regression With Continuous Optimization
An Overview of Methodologies and Challenges in Sentiment Analysis on Social Networks
Evaluation of Optimum and Coherent Economic-Capital Portfolios Under Complex Market Prospects
Data-Driven Stochastic Optimization for Transportation Road Network Design Under Uncertainty
Examining Visitors’ Characteristics and Behaviors in Tourist Destinations Through Mobile Phone Users’ Location Data
Machine Learning for Smart Tourism and Retail
Predictive Analysis of Robotic Manipulators Through Inertial Sensors and Pattern Recognition
Call Masking: A Worrisome Trend in Nigeria’s Telecommunications Industry
An Optimized Three-Dimensional Clustering for Microarray Data
Identifying Patterns in Fresh Produce Purchases: The Application of Machine Learning Techniques
Urban Spatial Data Computing: Integration of GIS and GPS Towards Location-Based Recommendations