Название: Secure Data Mining Автор: Jocelyn O. Padallan Издательство: Arcler Press Год: 2022 Страниц: 244 Язык: английский Формат: pdf (true) Размер: 10.1 MB
Data mining is a process to extract 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 database, 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, a number of 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 as 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. A number of approaches of 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 the presented in this chapter. Chapter 4 discusses the application of secure data mining in fraud detection. This chapter gives overview of the existing fraud detection systems and compares it 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.
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 detailed overview about the crime detection techniques used in data mining which include Self-Organizing Map Neural Network, Crime Matching etc. Chapter 6 is dedicated to the interdisciplinary nature of the data mining with telecommunication. 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 interconnection between data mining and security systems. Role of data mining in security systems and real-time data mining-based intrusion detection systems are explored in this chapter. Finally, Chapter 8 gives insight about 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. Interdisciplinary nature of the 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 the readers from diverse fields of data security along with the researchers and experts of data mining.
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