Data Mining 2:
Frequent Pattern Mining

Lecture in Summer 2018

Overview

• Description
• Times and Rooms
• Lecturer
• Exam Admission
• Lecture Slides
• Exercise Sheets
• Literature

Description

On this web page you can find information about the lecture "Data Mining 2: Frequent Pattern Mining" that is given by Christian Borgelt in summer 2018 at the University of Konstanz. This page will be updated in the course of the semester.

Type of activity:		Lecture/Exercise Lesson
Semester week hours:		2+2
Credits:		6
Language:		German or English according to preference
Requirements:		Data Mining 1 or Analysis and Visualization of Information
Certificate:		30 minutes oral exam (or a written exam depending on the number of students)

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

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Times and Rooms

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Lecturer

If you have questions about the lecture or the exercises, please refer (by email or in person in office Z811) to:

• Christian Borgelt, christian@borgelt.net

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Exam

Exam Admission

The lecture is accompanied by exercise sheets. At the beginning of each exercise lesson, the exercises are voted for. By voting for an exercise, one expresses one's willingness to present something about it. (Suggestions for a solution will be discussed, they need not be correct right away.) To the (oral) exam will be admitted, who

• voted for at least half of the exercises and
• presented something for at least two exercises.

Exam

The exam will be held orally, date and time to be arranged individually.

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Lecture Slides

Lecture slides (in English, version 2018.05.29, 540 slides).

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Exercise Sheets

Here you can find all already prepared exercise sheets as well as the corresponding earliest exercise lesson date at which these exercise sheets will be discussed. However, the exercise sheets may, depending on how the exercise lessons proceed, be (partially) discussed later than the stated date.

• Exercise Sheet 01: fpm01_eng.pdf fpm01_eng.tex (from 2018.04.26)
• Exercise Sheet 02: fpm02_eng.pdf fpm02_eng.tex (from 2018.05.03)
• Exercise Sheet 03: fpm03_eng.pdf fpm03_eng.tex (from 2018.05.14)
• Exercise Sheet 04: fpm04_eng.pdf fpm04_eng.tex (from 2018.05.24)
• Exercise Sheet 05: fpm05_eng.pdf fpm05_eng.tex (from 2018.06.04)
• Exercise Sheet 06: fpm06_eng.pdf fpm06_eng.tex (from 2018.06.11)
• Exercise Sheet 07: fpm07_eng.pdf fpm07_eng.tex (from 2018.06.18)
• Exercise Sheet 08: fpm08_eng.pdf fpm08_eng.tex (from 2018.06.25)
• Exercise Sheet 09: fpm09_eng.pdf fpm09_eng.tex (from 2018.07.02)
• Exercise Sheet 10: fpm10_eng.pdf fpm10_eng.tex (from 2018.07.09)
• Exercise Sheet 11: fpm11_eng.pdf fpm11_eng.tex (from 2018.07.16)
• METAPOST sources for figures: fpmex.mp moltg.mp

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Literature

• Frequent Pattern Mining
  C.C. Aggarwal and J. Han (eds.)
  Springer-Verlag, New York, NY, USA 2014

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