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Author: Admin | 2025-04-28
Chapter 1: Introduction to Data Mining 1.1 Definition of Data Mining 1.2 How Does Data Mining Work? 1.3 Architecture of Data Mining 1.4 Kinds of Data that can be mined 1.5 Data Mining Functionalities 1.6 Types of Data Mining Systems 1.7 Advantages of Data Mining 1.8 Disadvantages of Data Mining 1.9 Ethical Issues in Data Mining Chapter 2: Data Exploration 2.1 Data 2.2 Data Visualization Chapter 3: Data Preprocessing 3.1 Why Preprocessing? 3.2 Data Cleaning 3.3 Data Integration 3.4 Data Reduction 3.5 Data Transformation 3.6 Data Discretization and Concept Hierarchy Generation Chapter 4: Classification 4.1 Basic Concepts 4.2 Classification Methods 4.3 Prediction 4.4 Model Evaluation and Selection 4.5 Combining Classifiers (Ensemble Methods) Chapter 5: Clustering 5.1 Introducing Cluster Analysis 5.2 Clustering MethodologiesChapter 6: Outlier Analysis 6.1 Real-World Applications 6.2 Types of Outliers 6.3 Outlier Challenges 6.4 Outlier Detection Approaches 6.5 Outlier Detection Methods 6.6 Proximity-Based Outlier Analysis 6.7 Clustering-Based Outlier AnalysisChapter 7: Frequent Pattern Mining 7.1 Market Basket Analysis 7.2 Efficient and Scalable Frequent Item set Mining Methods 7.3 Mining Multilevel and Multidimensional Association Rules 7.4 Association Mining to Correlation Analysis Chapter 8: Introduction to Business Intelligence8.1 Data, Information and Knowledge 8.2 Defining Business Intelligence 8.3 Important Factors in Business Intelligence 8.4 Business Intelligence Architecture 8.5 Business Intelligence Framework 8.6 Role of Mathematical Models in BI 8.7 Factors Responsible for a Successful BI Project 8.8 Development of BI System 8.9 Obstacles to Business Intelligence in an Organization 8.10 Ethics and Business Intelligence Chapter 9: Decision Support System9.1 Concept of Decision Making 9.2 Techniques of Decision Making 9.3 Understanding Decision Support System (DSS) 9.4 Evolution of Information System 9.5 Development of Decision Support System 9.6 Application of DSS 9.7 Role of Business Intelligence in Decision Making Chapter 10: BI and Data Mining Applications10.1 ERP and Business Intelligence 10.2 BI Applications in CRM 10.3 BI Applications in Marketing 10.4 BI Applications in Logistics and Production 10.5 Role of BI in Finance 10.6 BI Applications in Banking 10.7 BI Applications in Telecommunications 10.8 BI Applications in Fraud Detection 10.9 BI Applications in Clickstream Mining 10.10 BI Applications in the Retail Industry Summary Review
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