**Lecture Notes**: The lecture notes have last been updated on the 27th of November and now cover lectures 1-9.

**Exams**: The exams are oral exams. We offer times for oral exams on the 4th, 6th and 8th of February. After enrolling for the exam in BASIS, please contact Christiane Andrade to schedule the time of your exam.

**Note**: If you attend the lecture and have not done so yet, please send me a short email to this email address. Thank you!

The content of this lecture is the theoretical analysis of approximation algorithms for cluster analysis, in particular covering the following areas:

- Approximation algorithms for k-center, k-median and k-means
- Different techniques for approximation algorithms, including ILP-based techniques and local search
- Clustering of Big Data and in Data Streams
- Analysis of common clustering heuristics
- Practically efficient methods with theoretical guarantees

When | Where | Start | Lecturer |
---|---|---|---|

Tuesday, 10:15-11:45 | INF / Room 2.078 | October 09th | Schmidt |

- Problem Set 1 (hand in until October 16th, to be discussed October 17th and October 19th)
- Problem Set 2 (hand in until October 23th, to be discussed October 24th and October 26th)
- Problem Set 3 (hand in until October 30th, to be discussed October 31st and November 2nd)
- Problem Set 4 (hand in until November 6th, to be discussed November 7th and November 9th)
- November 14th and November 16th tutorials were for repetition and questions and without a new exercise sheet
- Problem Set 5 (hand in until November 20th, to be discussed November 21st and November 23rd)
- Problem Set 6 (hand in until November 27th, to be discussed November 28th and November 30th)
- Problem Set 7 (hand in until December 4th, to be discussed December 5th and December 7th)
- Problem Set 8 (hand in until December 11th, to be discussed December 12th and December 14th)
- Problem Set 9 (hand in until December 18th, to be discussed December 19th and December 21th)

Date | |
---|---|

October 09 | 1 Introduction 2 The happy world of k-center 2.1 Definition 2.2 A simple and elegant 2-approximation |

October 16 | 2.3 A matching lower bound 2.4 Incremental and hierarchical clustering |

October 23 | 2.4 continued: Proof 2.5 Another elegant 2-approximation |

October 30 | 2.6 A streaming algorithm for k-center |

November 6 | –cancelled due to illness– |

November 13 | 2.7 The k-center problem with outliers 2.8 Fair k-center |

November 20 | 2.8 Fair k-center (ctd) 3 The exciting world of k-means 3.1 Definition 3.2 Lloyd's algorithm |

November 27 | 3.3 The k-means++ algorithm 3.3.1 D2-sampling as a bicriteria approximation |

December 4 | 3.3.2 A glimpse on the analysis of k-means++ 3.4 Dimensionality reduction |

December 11 | 3.4.1 The Johnson-Lindenstrauss Lemma |

December 18 | 3.4.2 The Singular Value Decomposition |

The lecture notes cover the content of the lecture and are updated after each lecture.