Padallan J.O. Secure Data Mining

Arcler Press, 2022. — 244 p.Data mining is a process of extracting useful knowledge from large amounts of data. To conduct data mining, we often need to collect data. However, privacy concerns may prevent people from sharing the data and some types of information about the data. How we conduct data mining without breaching data privacy presents a challenge. Secure Data Mining provides solutions to the problem of data mining without compromising data privacy. This professional book is designed for practitioners and researchers in industry, as well as a secondary textbook for advanced-level students in Computer Science.Every sphere of human life is burdened with a huge amount of databases, and this bulky database gives rise to a need for tools powerful enough to transform this data into valuable knowledge. To meet the demands of the database, several ways were explored by the researchers to develop mechanisms and methods in the areas of pattern recognition, neural nets, machine learning, data visualization, statistical data analysis, etc. The researchers have developed from the endeavors a new field of research, often termed data mining and knowledge discovery.Fundamentals and basic concepts regarding data mining are given in Chapter 1 which include data types, information gained from the data, and usefulness of the data mined. Chapter 2 provides detailed knowledge about the security of the data in the process of data mining. several approaches to security including classification and detection of data, clustering of data, intrusion detection systems, etc.. are discussed in this chapter. Classification approaches of the data are discussed in Chapter 3 of this book. Categorization of data and categorization techniques, preprocessing of data, and feature selection are presented in this chapter. Chapter 4 discusses the application of secure data mining in fraud detection. This chapter gives an overview of the existing fraud detection systems and compares them with the secure system of fraud detection. The techniques used for fraud detection including Bayesian networks, Rule-based algorithms, Artificial Neural networks, etc.. are discussed in detail in this chapter.The application of data mining in crime detection is presented in Chapter 5 of this book. This chapter starts with the introduction of intelligent crime analysis and then gives a detailed overview of the crime detection techniques used in data mining which include Self-Organizing Map Neural Networks, Crime Matching, etc.. Chapter 6 is dedicated to the interdisciplinary nature of data mining with telecommunication. The role of data mining in telecommunication, multidimensional association and sequential pattern analysis, use of visualization tools in telecommunication data analysis, etc. are discussed in detail. Chapter 7 presents the interconnection between data mining and security systems. The role of data mining in security systems and real-time data mining-based intrusion detection systems is explored in this chapter. Finally, Chapter 8 gives insight into the recent trends and future projections of data mining. A comparison of the past data mining trends with the present and future trends is given in this chapter. The interdisciplinary nature of data mining with other fields of engineering and science, finance, and retail industries is also discussed in this chapter.This book can serve as a valuable tool for readers from diverse fields of data security along with researchers and experts in data mining.