Spring 2016 / DM843
One trend can be observed over almost all fields of informatics: we have to cope with an ever-increasing amount of available data of all kinds. This amount of data renders it impossible to inspect the dataset "by hand", or even deduce knowledge from the given data, without sophisticated computer aided help. In this course we will discuss one of the most common mechanism of unsupervised machine learning for investigating datasets: Clustering. Clustering separates a given dataset into groups of similar objects, the clusters, and thus allows for a better understanding of the data and their structure. We discuss a number of clustering methods and their application to various different fields such as biology, economics or sociology.