Contents

1. Introduction to Data Warehousing

1. Data Warehousing and Business Analysis

2. Data Warehousing Components

3. Building a Data Warehouse

4. Data Warehouse Architecture

5. DBMS Schemas for Decision Support

6. Data Extraction, Clean-up and Transformation Tools

7. Metadata

8. Reporting

9. Query Tools and Applications

10. Online Analytical Processing (OLAP)

11. Multidimensional Data Analysis

2. Introduction to Data Mining Systems

1. Knowledge Discovery Process

2. Data Mining Techniques

2.1 Issues in Data Mining Techniques

2.2 Applications of Data Mining

3. Data Objects and Attribute Types

4. Statistical Description of Data

5. Data Pre-processing

6. Major Tasks in Data Pre-processing

6.1 Data Cleaning

6.2 Noisy Data

6.3 Data Integration

6.4 Data Reduction

6.5 Data Transformation and Data Discretization

6.6 Data Visualization

6.7 Data Similarity and Dissimilarity Measures

3. Data Mining Tasks

1. Introduction

2. Mining Association Rules in Large Databases

3. Association Rule Mining

4. Market Basket Analysis: Mining A Road Map

5. The Apriori Algorithm: Finding Frequent Item sets Using Candidate Generation

6. The Apriori Algorithm

7. Generating Association Rules from Frequent Itemsets

8. Improving the Efficiently of Apriori Algorithm

9. Mining Frequent Item Sets without Candidate Generation

10. Multilevel Association Rules

11. Approaches to Mining Multilevel Association Rules

12. Mining Multidimensional Association Rules for Relational Database and Data Warehouses

13. Multidimensional Association Rules

14. Mining Quantitative Association Rules

15. Mining Distance-Based Association Rules

16. From Association Mining to Correlation Analysis

4. Classification and Clustering

1. Introduction

2. Problem Definition

3. General Approaches to Solving a Classification Problem

4. Evaluation of Classifiers, Classification Techniques

5. Decision Trees-Decision Tree Construction

6. Methods for Expressing Attribute Test Condition

7. Measures for Selecting the Best Split

8. Algorithm for Decision Tree Induction

9. Naïve-Bayes Classifier

10. Bayesian Belief Networks

11. K-nearest Neighbor Classification-Algorithm and Characteristics

12. Clustering Overview

13. Evaluation of Clustering Algorithms

14. Partitioning Clustering