Seminar on Algorithms: Clustering

 

Clustering is a fundamental task in data science that often arises in real-world applications. In the past decades several alternative formulations of the clustering problem have been proposed. These have led to a variety of algorithms, both exact and approximate, using a broad range of optimization techniques such as local search, core-sets, linear programming, etc. Studying the inherent hardness of clustering has led to deep theoretical results, combining insights from geometry and graph theory.

In this seminar/reading group we will study representative and recent results related to clustering, with a focus on algorithmic and complexity aspects. We present/discuss one or two papers each week.

Requirements: ALP3/HA or similar algorithmic background, mathematical maturity.

welcome announcement

 

introduction and topics slides

 

Presentation calendar:

Date Topic Presenter
5th Nov k-center greedy approximation and hardness Clea Peter
12th Nov

k-means++

Wenli Xu
  NP-hardness of k-means Florian Alex
19th Nov hierarchical clustering Yao Zheng
  impossibility of clustering Niclas Schwarzlose
26th Nov sampling approximation for k-means

Clemes Mosig

  LP-relaxation for median clustering Hannah Troppens