20 citations · 23 across the 3 of their papers we have counts for
5 papers · 1 filter
On Coreset Constructions for the Fuzzy -Means Problem
Johannes Blömer, Sascha Brauer, Kathrin Bujna
The fuzzy -means problem is a popular generalization of the well-known -means problem to soft clusterings. We present the first coresets for fuzzy -means with size linear…
Hard-Clustering with Gaussian Mixture Models
Johannes Blömer, Sascha Brauer, Kathrin Bujna
Training the parameters of statistical models to describe a given data set is a central task in the field of data mining and machine learning. A very popular and powerful way of pa…
Complexity and Approximation of the Fuzzy K-Means Problem
Johannes Blömer, Sascha Brauer, Kathrin Bujna
The fuzzy -means problem is a generalization of the classical -means problem to soft clusterings, i.e. clusterings where each points belongs to each cluster to some degree. A…
Adaptive Seeding for Gaussian Mixture Models
Johannes Blömer, Kathrin Bujna
We present new initialization methods for the expectation-maximization algorithm for multivariate Gaussian mixture models. Our methods are adaptions of the well-known -means++ i…
A Theoretical and Experimental Comparison of the EM and SEM Algorithm
Johannes Blömer, Kathrin Bujna, Daniel Kuntze
In this paper we provide a new analysis of the SEM algorithm. Unlike previous work, we focus on the analysis of a single run of the algorithm. First, we discuss the algorithm for g…