Contents

1. Know your Data and Data Pre-processing

1. Data Objects and Attribute Types

2. Data Pre-processing

3. Data Integration

4. Data Reduction

5. Data Transformation and Data Discretization 

2. Data Warehousing and Online Analytical Processing

1. Introduction to Data Warehousing

2. Data Warehouse Life Cycle

3. Extract Transform and Load (ETL)

4. Dimension Modeling 

3. Association Mining Rules Basic Concepts, Algorithms

1. Data Mining

2. Introduction to Machine Learning

3. Issues and Challenges in Data Mining

4. Data Mining Techniques

5. Introduction to Association Rule Mining 

4. Classification and Prediction

1. Introduction to Classification

2. Bayesian Classification

3. Rule-Based Classification

4. k-Nearest-Neighbor Classifiers (Lazy Learners)

5. Artificial Neural Network

6. Support Vector Machines

7. Associative Classification 

5. Clustering Techniques

1. Introduction to Cluster Analysis

2. Major Clustering Methods

3. Quality and Validity of Cluster Analysis

4. Outliers 

6. Other Approaches of Data Mining and Data Mining Applications

1. Discovery of Sequential Patterns

2. Bayesian Network

3. Genetic Algorithms (GA)

4. Rough Set and Fuzzy Set Approach

5. Text Mining

6. Temporal and Spatial Data Mining

7. Current Trends in Data Mining

8. Data Visualization