1CHAPTER 1 Introduction to Data Mining
527.1 Linear Regression
21.1 Definition and Scope of Data Mining
537.2 Polynomial Regression
31.2 Evolution of Data Mining
547.3 Ridge and Lasso Regression
41.3 Importance in Decision Making
557.4 Logistic Regression
51.4 Applications in Various Industries
567.5 Evaluation Metrics and Applications
61.5 Challenges and Opportunities
577.6 Summary
71.6 Summary
586.7 Exercise : References
81.7 Exercise : References
59CHAPTER 8 Dimensionality Reduction
9CHAPTER 2 Foundations of Data Mining
608.1 Principal Component Analysis (PCA)
102.1 Basics of Statistics
618.2 t-Distributed Stochastic Neighbor Embedding (t-SNE)
112.2 Probability and Random Variables
628.3 Singular Value Decomposition (SVD)
122.3 Linear Algebra for Data Analysis
638.4 Feature Extraction Techniques
132.4 Database Systems and Concepts
648.5 Visualization Techniques in Dimensionality Reduction
142.5 Machine Learning Fundamentals
658.6 Summary
152.6 Mathematical Models in Data Mining
668.7 Exercise: References
162.7 Summary
67CHAPTER 9 Time Series Analysis and Forecasting
172.8 Exercise: References
689.1 Basics of Time Series Data
18CHAPTER 3 Data Exploration and Preprocessing
699.2 Time Series Decomposition
193.1 Data Collection Techniques
709.3 Autoregressive Integrated Moving Average (ARIMA)
203.2 Data Cleaning and Transformation
719.4 Exponential Smoothing Methods
213.3 Handling Missing Values
729.5 Forecast Evaluation Metrics
223.4 Outlier Detection and Treatment
739.6 Applications in Business Forecasting
233.5 Feature Scaling and Selection
749.7 Summary
243.6 Exploratory Data Analysis (EDA)
759.8 Exercise: References
253.7 Summary
76CHAPTER 10 Ethical Considerations in Data Mining
263.8 Exercise : References
7710.1 Privacy Concerns
27CHAPTER 4 Classification Algorithms
7810.2 Bias and Fairness in Algorithms
284.1 Decision Trees
7910.3 Transparency and Explainability
294.2 Support Vector Machines (SVM)
8010.4 Consent and Data Ownership
304.3 k-Nearest Neighbors (k-NN)
8110.5 Legal and Regulatory Frameworks
314.4 Naive Bayes Classification
8210.6 Responsible Data Mining Practices
324.5 Ensemble Methods and Evaluation Metrics
8310.7 Summary
334.6 Summary
8410.8 Exercise : References
344.7 Exercise : References
85CHAPTER 11 Future Trends in Data Mining
35CHAPTER 5 Clustering Techniques
8611.1 Artificial Intelligence Integration
365.1 K-Means Clustering
8711.2 Explainable AI (XAI)
375.2 Hierarchical Clustering
8811.3 Federated Learning
385.3 DBSCAN
8911.4 Quantum Computing and Data Mining
395.4 Gaussian Mixture Models
9011.5 Automated Machine Learning (AutoML)
405.5 Self-Organizing Maps
9111.6 Summary
415.6 Summary
9211.7 Exercise : References
425.7 Exercise : References
93CHAPTER 12 Case Studies and Practical Applications
43CHAPTER 6 Association Rule Mining
9412.1 Healthcare Analytics
446.1 Market Basket Analysis
9512.2 Financial Fraud Detection
456.2 Apriori Algorithm
9612.3 Customer Segmentation in Marketing
466.3 FP-Growth Algorithm
9712.4 Predictive Maintenance in Manufacturing
476.4 Rule Pruning and Optimization
9812.5 Social Media Analytics
486.5 Applications in Recommender Systems
9912.6 Emerging Applications in Data Mining: 12.6 Summary
496.6 Summary
10012.7 Exercise : References
506.7 Exercise : References
101Glossary
51CHAPTER 7 Regression Analysis in Data Mining
102Index
