Frequent Pattern Mining

Lecture in Summer 2017

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 2017 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+1 | |

Credits: |
5 | |

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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Day of the Week | Time | Room | Start | |
---|---|---|---|---|

Lecture | Monday | 17:00 - 18:30 hours | R512 | 24.04.2017 |

Exercise Lesson | Tuesday | 13:30 - 15:00 hours | M1101 | 09.05.2017 |

(exercise lesson only every other week) |

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

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**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 (in English, version 2017.06.07, 536 slides).

fpm.pdf | (3509 kb) | one slide per page |

fpm4.pdf | (3358 kb) | four slides per page, two by two |

fpm4s.pdf | (3356 kb) | four slides per page, four by one |

fpm.zip | (12247 kb) | LaTeX and METAPOST source files |

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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 1: fpm01_eng.pdf fpm01_eng.tex (from 2017.05.09)
- Exercise Sheet 2: fpm02_eng.pdf fpm02_eng.tex (from 2017.05.23)
- Exercise Sheet 3: fpm03_eng.pdf fpm03_eng.tex (from 2017.06.06)
- Exercise Sheet 4: fpm04_eng.pdf fpm04_eng.tex (from 2017.06.20)
- Exercise Sheet 5: fpm05_eng.pdf fpm05_eng.tex (from 2017.07.04)
- Exercise Sheet 6: fpm06_eng.pdf fpm06_eng.tex (from 2017.07.18)
- METAPOST sources for figures: fpmex.mp moltg.mp

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**Frequent Pattern Mining**

C.C. Aggarwal and J. Han (eds.)

Springer-Verlag, New York, NY, USA 2014

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