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1 - Data mining
1.1 - What is data mining?
1.2 - Data mining background
1.2.1 - Inductive learning
1.2.2 - Statistics
1.2.3 - Machine Learning
1.2.4 - Differences between Data Mining and Machine Learning
1.3 - Data Mining Models
1.3.1 - Verification Model
1.3.2 - Discovery Model
1.4 - Data Warehousing
1.4.1 - Characteristics of a data warehouse
1.4.2 - Processes in data warehousing
1.4.3 - Data warehousing and OLTP systems
1.4.4 - The Data Warehouse model
1.4.5 - Problems with data warehousing
1.4.6 - Criteria for a data warehouse
1.5 - Data mining problems/issues
1.5.1 - Limited Information
1.5.2 - Noise and missing values
1.5.3 - Uncertainty
1.5.4 - Size, updates, and irrelevant fields
1.6 - Potential Applications
1.6.1 - Retail/Marketing
1.6.2 - Banking
1.6.3 - Insurance and Health Care
1.6.4 - Transportation
1.6.5 - Medicine
2 - Data Mining Functions
2.1 - Classification
2.2 - Associations
2.3 - Sequential/Temporal patterns
2.4 - Clustering/Segmentation
2.4.1 - IBM - Market Basket Analysis example
3 - Data Mining Techniques
3.1 - Cluster Analysis
3.2 - Induction
3.2.1 - decision trees
3.2.2 - rule induction
3.3 - Neural networks
3.4 - On-line Analytical processing
3.4.1 - OLAP Example
3.4.2 - Comparison of OLAP and OLTP
3.5 - Data Visualisation
4 - Siftware - past and present developments
4.1 - New architectures
4.1.1 - Obstacles
4.1.2 - The key
4.1.3 - Oracle was first
4.1.4 - Red Brick has a strong showing
4.1.5 - IBM is...
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