Data Mining Models, Second Edition

Data mining has become the fastest growing topic of interest in business programs in the past decade. The massive growth in data generation, often called big data, in science (weather, ecology, biosciences, any scientific field), social studies (politics, health, many other fields), as well as business (real-time data in retail from cash registers, in supply chains from vendor to retail, financial to include banking, investment, and insurance, and less conventional areas such as human resource management). In response, many schools have created (or are creating) Masters programs in business analytics. This book is intended to first describe the benefits of data mining in business, describe the process and typical business applications, describe the workings of basic data mining tools, and demonstrate each with widely available free software. This book is designed for masters students. But that overlaps with business professionals as most new masters programs in business analytics are delivered on-line. Data mining has become the fastest growing topic of interest in business programs in the past decade. This book is intended to describe the benefits of data mining in business, the process and typical business applications, the workings of basic data mining models, and demonstrate each with widely available free software. The book focuses on demonstrating common business data mining applications. It provides exposure to the data mining process, to include problem identification, data management, and available modeling tools. The book takes the approach of demonstrating typical business data sets with open source software. KNIME is a very easy-to-use tool, and is used as the primary means of demonstration. R is much more powerful and is a commercially viable data mining tool. We also demonstrate WEKA, which is a highly useful academic software, although it is difficult to manipulate test sets and new cases, making it problematic for commercial use. Acknowledgments 7 Chapter 1 Data Mining in Business 8 Chapter 2 Business Data Mining Tools 18 Chapter 3 Data Mining Processes and Knowledge Discovery 28 Chapter 4 Overview of Data Mining Techniques 40 Chapter 5 Data Mining Software 56 Chapter 6 Regression Algorithms in Data Mining 89 Chapter 7 Neural Networks in Data Mining 116 Chapter 8 Decision Tree Algorithms 135 Chapter 9 Scalability 163 Notes 175 References 176 Index 178