1Chapter-1
35Chapter-5
2Introduction to Data Mining
36Data Mining and Big Data
31.1 Definition and Scope
375.1 Challenges in Big Data Mining
41.2 Historical Overview
385.2 Techniques for Big Data Mining
51.3 Applications of Data Mining
39Chapter-6
61.4 Data Mining Process Overview
40Feature Engineering and Selection
7Chapter-2
416.1 Feature Engineering
8Data Exploration and Preprocessing
426.2 Feature Selection
92.1 Data Cleaning
436.2.1 Wrapper Methods: 6.2.2 Embedded Methods
102.2 Data Transformation
44Chapter-7
112.3 Data Reduction
45Text and Sentiment Analysis
122.4 Descriptive Statistics
467.1 Text Mining Concepts
132.5 Visualization Techniques
477.2 Natural Language Processing
14Chapter-3
487.3 Text Classification
15Supervised Learning
497.4 Sentiment Analysis
163.1 Classification Concepts
50Chapter-8
173.2 Decision Trees
51Advanced Topics
183.3 Support Vector Machines
528.1 Time Series Analysis
193.4 Naive Bayes
538.1.1 Trends and Seasonality: 8.1.2 Forecasting Models
203.5 Ensemble Methods
548.2 Social Network Analysis
213.5.1 Bagging: 3.5.2 Boosting
558.3 Predictive Modelling: 8.3.1 Regression Analysis
223.6 Model Evaluation
56Chapter-9
233.6.1 Accuracy Metrics
57Ethical Considerations in Data Mining
243.6.2 Precision, Recall, F1 Score
589.1 Privacy Concerns
253.6.3 ROC Curve
599.2 Bias and Fairness: 9.3 Transparency and Accountability
26Chapter 4
60Chapter-10
27Unsupervised Learning
61Applications of Data Mining
284.1 Clustering Concepts
6210.1 Healthcare
294.2 K-Means Clustering
6310.2 Finance
304.3 Hierarchical Clustering
6410.3 E-commerce
314.4 DBSCAN
6510.4 Social Media
324.5 Frequent Pattern Mining
66Glossary
334.6 Association Rules
67Index
344.6.1 Apriori Algorithm: 4.6.2 FP-Growth Algorithm
