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Found 5 papers, 0 papers with code
On Coreset Constructions for the Fuzzy $K$-Means Problem
The fuzzy $K$-means problem is a popular generalization of the well-known $K$-means problem to soft clusterings.
Hard-Clustering with Gaussian Mixture Models
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.
Complexity and Approximation of the Fuzzy K-Means Problem
We complement these results with a randomized algorithm which imposes some natural restrictions on the input set and whose runtime is comparable to some of the most efficient approximation algorithms for $K$-means, i. e. linear in the number of points and the dimension, but exponential in the number of clusters.
Adaptive Seeding for Gaussian Mixture Models
Our methods are adaptions of the well-known $K$-means++ initialization and the Gonzalez algorithm.
A Theoretical and Experimental Comparison of the EM and SEM Algorithm
In this paper we provide a new analysis of the SEM algorithm.
