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Data Mining

An Introduction

Student Notes

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 still the largest

4.1.6 - INFORMIX is online with 8.0

4.1.7 - Sybase and System 10

4.1.8 - Information Harvester

4.2 - Vendors and Applications

4.2.1 - Information Harvesting Inc

4.2.2 - Red Brick

4.2.3 - Oracle

4.2.4 - Informix - Data Warehousing

4.2.5 - Sybase

4.2.6 - SG Overview

4.2.7 - IBM Overview

5 - Data Mining Examples

5.1 - Bass Brewers

5.2 - Northern Bank

5.3 - TSB Group PLC

5.4 - BeursBase, Amsterdam

5.5 - Delphic Universities

5.6 - Harvard - Holden

5.7 - J.P. Morgan