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.
Author(s): Jocelyn D. Padallan
Publisher: AclerPress
Year: 2022
Language: English
Pages: 244
Cover
Title Page
Copyright
ABOUT THE AUTHOR
TABLE OF CONTENTS
List of Figures
Lis of Tables
Preface
Chapter 1 Fundamentals of Data Mining
1.1. Introduction
1.2. Moving Toward the Information Era
1.3. Data Mining
1.4. What Type of Data Are We Gathering?
1.5. What Kind of Data Can Be Mined?
1.6. What May Be Discovered?
1.7. Is Everything you Discover Fascinating and Beneficial?
1.8. Different Types of Data Mining Setups
1.9. Problems in Data Mining
References
Chapter 2 Security in Data Mining
2.1. Introduction
2.2. Classification and Detection Using Data mining Techniques
2.3. Clustering
2.4. Privacy-Preserving Data Mining (PPDM)
2.5. Intrusion Detection System (idsIDS)
2.6. Classification of Phishing Websites
2.7. Artificial Neural Networks (ANN)
2.8. Outlier Detection/Anomaly Detection
References
Chapter 3 Classification Approaches in Data Mining
3.1. Introduction
3.2. Preprocessing of Data
3.3. Selection of Feature
3.4. Categorization
3.5. Categorization Techniques
References
Chapter 4 Application of Secure Data Mining in Fraud Detection
4.1. Introduction
4.2. Existing Fraud Detection Systems
4.3. Applications
4.4. Model Performance
References
Chapter 5 Application of Data Mining in Crime Detection
5.1. Introduction
5.2. Fundamentals of Intelligent Crime Analysis
5.3. Components of the Planned Technique: Toward a Crime Matching Outline
References
Chapter 6 Data Mining in Telecommunication Industry
6.1. Introduction
6.2. Role of Data Mining in Telecommunication Industry
6.3. Data Mining and Telecommunication Industry
6.4. Data Mining Focus Areas in Telecommunication
6.5. A Learning System for Decision Support in Telecommunications – Case Study
6.6. Knowledge Processing in Control Systems
6.7. Data Mining for Maintenance of Complex Systems – A Case Study
Reference
Chapter 7 Data Mining In Security Systems
7.1. Introduction
7.2. Roles of Data Mining in Security Systems
7.3. Data Mining and Security Systems
7.4. Real-Time Data Mining-Based Intrusion Detection Systems
References
Chapter 8 Recent Trends and Future Projections of Data Mining
8.1. Introduction
8.2. A sequence of Data Mining: Time-Series, Symbolic and Biological Sequences
8.3. The search of Resemblance in the Time-Series Data
8.4. Analysis of Regression and Trend in the Time-Series Data
8.5. Advancement of Information and Social Networks
8.6. Data Mining Uses
8.7. Mining of Data and Society
8.8. Trends of Data Mining
References
Index
Back Cover
