Data Mining I

Overview

Data Mining denotes an actual and fast growing research and application field, which covers methods and software for the extraction of information from huge amounts of data:

This is mainly influencend by the the usage of automatic data gathering devices in business, science and administration and encompasses a number of different technical approaches, such as clustering, data summarization, association rules, and learning classification rules

Content

• Foundations
  • Introduction/Motivation
  • Development/Data Mining vs. Statistics
  • Process Models (-> CRISP-DM)
  • Data Warehouse & Data Mining, Business Intelligence
• Data Mining Tasks/Techniques
  • Data Description/Visualization
  • Segmentation
    • Cluster Analysis
    • Self-Organizing Maps
  • Link Analysis/Sequence Pattern Detection
    • Association Rules
    • Sequence Pattern
  • Classification
    • Decision Trees
    • Discriminant Analysis
    • Linear/Logistic Regression
    • Feedforward Backpropagation Networks
    • Support Vector Machine, ...

References (partly within the "Semesterapparat Möhring" in the library)