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Robust Cluster Analysis and Variable Selection

Gunter Ritter
€210.80
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advanced probabilistic clustering methods
Asymptotic Breakdown Point
Author_Gunter Ritter
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Breakdown Point
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Cluster Analysis
cluster validation techniques
clustering methods
data
Data Set
data trimming methods
Density Generators
Em Algorithm
Em Step
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feature selection algorithms
function
Geometric Arithmetic Inequality
Hausdorff Metric
Hellinger Distance
likelihood
Log Det
matrix
Mixture Model
mixture models
mixtures
Ml Criterion
Ml Estimator
Ml Parameter Estimation
multivariate statistics
Nonparametric Mixture Model
normal
Normal Mixture
pareto
Pareto Solutions
probabilistic clustering
Scale Invariant Estimators
scatter
set
solutions
Spurious Outliers
Steiner's Formula
Steiner’s Formula
Symmetrized Kullback Leibler Divergence
Unimodal Population
unsupervised learning
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Variable Selection

Product details

  • ISBN 9781439857960
  • Weight: 890g
  • Dimensions: 178 x 254mm
  • Publication Date: 02 Sep 2014
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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Description

Clustering remains a vibrant area of research in statistics. Although there are many books on this topic, there are relatively few that are well founded in the theoretical aspects. In Robust Cluster Analysis and Variable Selection, Gunter Ritter presents an overview of the theory and applications of probabilistic clustering and variable selection, synthesizing the key research results of the last 50 years.

The author focuses on the robust clustering methods he found to be the most useful on simulated data and real-time applications. The book provides clear guidance for the varying needs of both applications, describing scenarios in which accuracy and speed are the primary goals.

Robust Cluster Analysis and Variable Selection includes all of the important theoretical details, and covers the key probabilistic models, robustness issues, optimization algorithms, validation techniques, and variable selection methods. The book illustrates the different methods with simulated data and applies them to real-world data sets that can be easily downloaded from the web. This provides you with guidance in how to use clustering methods as well as applicable procedures and algorithms without having to understand their probabilistic fundamentals.
About Gunter Ritter

Dr. Gunter Ritter is an emeritus professor in the Department of Mathematics and Computer Science at the University of Passau. He is the author and coauthor of numerous research papers in scientific journals in the areas of measure theory, probability theory, queuing theory, statistics, pattern and image recognition, and Fourier analysis. He is a member of the International Federation of Classification Societies and its German branch GfKl as well as the German Mathematical Society.

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