1Chapter 1. Introduction to Data Mining
516.9 k-NN Algorithm
21.1 How Data Mining Works
526.10 Naïve Bays Algorithm
31.2 Types of Data Mining
536.11 Classification and Regressions Tree (CART)
41.3 Pre-requisite for Data Mining
546.12 Artificial Neural Network (ANN)
51.4 Data Mining Applications
556.13 48 Decision Trees
61.5 Challenges in Learning Data Mining
566.14 Summary
71.6 Data Mining and Statistics
576.15 Exercise
81.7 Summary
58Chapter 7. Naïve Bays
91.8 Exercise
597.1 Conditional Probability
10Chapter 2. Data Mining Techniques
607.2 Bayes Theorem
112.1 Classification
617.3 Naïve Bays Theorem
122.2 Clustering
627.4 Advantages and Disadvantages of Naïve Bays
132.3 Outlier
637.5 Assumptions of Naïve Bays
142.4 Sequential Patterns
647.6 Gaussian Naïve Bays
152.5 Prediction
657.7 Multinomial Naïve Bays
162.6 Association Rules
667.8 Bernoulli Naïve Bays
172.7 Summary
677.9 Python Implementation
182.8 Exercise
687.8 Applications of Naïve Bays
19Chapter 3. Data Visualization and Processing
697.9 Summary
203.1 Where can data visualization be used?
707.10 Exercise
213.2 Types of Data Visualization
71Chapter 8. Cluster Analysis
223.3 Data Visualization Best Practices
728.1 Why Clustering is Required in Data Mining
233.4 Stages of Data Processing
738.2 Clustering Methods
243.5 Methods of Data Processing
748.3 Summary
253.6 Output files obtained as Processed Data
758.4 Exercise
263.7 Summary
76Chapter 9. Anomalies
273.8 Exercise
779.1 Types of Anomalies
28Chapter 4. Statistics in Data Mining
789.2 Data Labels
294.1 Statistics
799.3 Output of Anomaly Detection
304.2 Data Mining
809.4 General Working Idea of Anomaly Detection
314.3 Data Mining and Statistics
81Techniques
324.4 Difference between Data Mining and Statistics
829.5 Commonly Used Anomaly Detection Algorithms
334.5 Linear Regression Analysis
839.6 Applications of Anomaly Detection
344.6 Summary
849.7 Summary
354.7 Exercise
859.8 Exercise
36Chapter 5. Python and Data Mining
86Chapter 10. Data Cube Technology
375.1 Data Mining Techniques
8710.1 How Data Cubes Work
385.2 Starting Data Mining with Python
8810.2 Classification of Data Cubes
395.3 Data Mining Algorithms in Python
8910.3 Uses of Data Cube Technology
405.4 Summary
9010.4 Summary
415.5 Exercise
9110.5 Exercise
42Chapter 6. Algorithms of Data Mining
92Chapter 11. Data Mining Trends and Research Frontiers
436.1 Introduction
9311.1 Mining Complex Data Types
446.2 C4.5 Algorithm
9411.2 Data Mining Applications
456.3 K-Means Algorithm
9511.3 Data Mining and the Society
466.4 Support Vector Machines (SVM)
9611.4 Data Mining Trends
47 6.5 Apriori Algorithm
9711.5 Summary
486.6 Expectation-Maximization Algorithm
9811.6 Exercise
49 6.7 PageRank Algorithm
99Glossary
506.8 AdaBoost Algorithm
100Index
