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Overview

Credits: 3 SWS, 5 Credits

Course type : Lecture

Language: english

Lecture: Mo, 17:00 - 18:30, R512 (weekly)

Exercise:

Tue, 13:30 - 15:00, M1101 (biweekly, 09.05., 16.05., etc.)

Slides etc.

More information concerning this lecture, including material and exercises, can be found on the following websites:

English

German

Description

Contents:

  • Frequent item set mining and association rule induction
  • Frequent sequence mining (discrete and interval data)
  • Frequent tree and graph mining
  • Efficient basic algorithms and data structures
  • Avoiding redundant search when analyzing structured data, especially with the help of canonical forms of the desired patterns
  • Approaches to evaluate and filter found patterns
  • Extensions of the basic algorithms for special applications
  • Application examples, especially for mining frequent graphs and sequences

Learning Objectives:

  • Knowledge of the basic algorithmic schemes and the most common concrete algorithms for finding frequent item sets
  • Understanding of the needed efficient data structures and processing methods
  • Insight into the special problems occurring in the analysis of structured data (sequences, trees, general graphs) and approaches to solve these problems
  • Ability to select an appropriate method to find frequent patterns depending on the application
  • Capability to develop efficient specialized algorithms to find frequent patterns

Important

Registration via StudIS required.