Data Mining I

Intended Audience

  • Master IM (Specialisation)
  • Master Information Systems Research (Specialisation)
  • Master Computer Science (Key aspects in computer science)

Lecturer

  • Michael Möhring

Exam

  • written examination + excercises (6 ECTS-Points)

Related event

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:

 

Data Mining, is the exploration and analysis, by automatic or semiautomatic means, of large
quantities of data in order to discover meaningful patterns and rules
(Berry/Linoff, 1997)

 

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)