The purpose of this book is to discuss data mining, which is a subset of data science, from a variety of perspectives. With the technological advances of recent years, new software and hardware-based systems are available in most business environments. With these systems, data production continues to increase in personal, corporate, commercial and many other areas. Information systems convert raw data, which alone are not so meaningful, into information after the processes are applied. Database systems are necessary for the storage and management of the information generated. Revealing meaningful relationships hidden in a stack of high-volume data shows the function of data mining. Processing big data has become important to produce information that will support business decisions and be a strategic tool in today's competitive environment. In this context, the effectiveness of data mining applications is increasing day by day as a decision support system to develop marketing strategies in every sector by identifying customer behavior and target groups.
Author(s): Cem Ufuk Baytar
Series: Research Methodology and Data Analysis
Publisher: Nova Science Publishers
Year: 2022
Language: English
Pages: 154
City: New York
Contents
Preface
Acknowledgments
Chapter 1
Data Analytics Applied to the Human Resources Industry
Abstract
Introduction
Data Analytics
Human Resources Analytics
Conclusion
References
Chapter 2
Toxicogenomics Data Mining as a Promising Prioritization Tool in Toxicity Testing
Abstract
Introduction
Useful Databases and Tools for Data Mining in Toxicology
Data Mining Examples
Advantages
Limitations
Conclusion
Acknowledgments
References
Chapter 3
Applications of Data Mining Algorithms for Customer Recommendations in Retail Marketing
Abstract
Introduction
Literature Review
Methodology
Findings and Results
Conclusion
References
Chapter 4
Analysis of Customer Churn in Banking Industry Using Data Mining
Abstract
Introduction
Digitalization and Online Banking
Customer Relations Management and Data Analysis
Customer Loyalty and Data Analysis
Tools and Methodology
Understanding the Data
Data Preparation
Data Modeling
Decision Tree Algorithm
The Random Forest Algorithm
Artificial Neural Networks
Conclusion
References
Chapter 5
The Crowdsourcing Concept-Based Data Mining Approach Applied in Prosumer Microgrids
Abstract
Introduction
Crowdsourcing Energy System
The Problem Formulation
The Prosumer Profiling Using Data Mining Method
Optimal Allocation of Prosumers in Local Microgrids
Results and Discussion
Conclusion
References
Chapter 6
Active Learning
Abstract
Introduction
Query Strategies
Use Case
Industry and Robotics
Healthcare
Cybersecurity
Conclusion
References
Chapter 7
Prediction of General Anxiety Disorder Using Machine Learning Techniques
Abstract
Introduction
Literature Review
Background
Materials and Methods
SVM
Decision Tree
ANN
RF
KNN
Performance Metrics
Data Description
Normalization Filter
Experiments and Results
ANN Results
K-Nearest Neighbor Results
Decision Tree Results
Random Forest Results
Comparison of Performance Metrics for ANN, KNN, DT, RF, SVM
Conclusion
References
Editor’s Contact Information
Index
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