The Future of Data Mining

Description

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. 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 Blank Page Blank Page