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Kuntze, Daniel: Practical algorithms for clustering and modeling large data sets : analysis and improvements. 2014
Inhalt
Dedication
Acknowledgments
Abstract
Zusammenfassung
Introduction
Cluster analysis
Parameter estimation
Outline of the thesis
On cluster analysis
Clustering Basics
The notion of similarity
Clustering quality
Problem Definitions
Analysis of Agglomerative Clustering
Preliminaries
Discrete k-center clustering
k-center clustering
Diameter k-clustering
The one-dimensional case
Lower bounds
On parameter estimation
Statistical models
The parameter estimation problem
General mixture models
Gaussian mixture models
Handy notions from probability theory
The classical EM Algorithm
Convergence of the EM algorithm
The EM algorithm for mixture distributions
The EM algorithm for Gaussian mixtures
The stochastic EM algorithm
The generic SEM algorithm
The SEM algorithm for mixture distributions
The SEM algorithm for Gaussian mixtures
Experimental analysis
Implementation
Data sets
Experiments
Results
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