Advances in Data Analysis and Classification View Homepage


Ontology type: schema:Periodical     


Journal Info

START YEAR

2007

PUBLISHER

Springer Berlin Heidelberg

LANGUAGE

en

HOMEPAGE

http://link.springer.com/journal/11634

Recent publications latest 20 shown

  • 2019-03-19 Robust and sparse k-means clustering for high-dimensional data
  • 2019-03-02 Enhancing techniques for learning decision trees from imbalanced data
  • 2019-03 Random effects clustering in multilevel modeling: choosing a proper partition
  • 2019-03 From here to infinity: sparse finite versus Dirichlet process mixtures in model-based clustering
  • 2019-03 Finite mixtures, projection pursuit and tensor rank: a triangulation
  • 2019-03 Finite mixture biclustering of discrete type multivariate data
  • 2019-03 Variable selection in model-based clustering and discriminant analysis with a regularization approach
  • 2019-03 Assessing trimming methodologies for clustering linear regression data
  • 2019-03 Clustering via finite nonparametric ICA mixture models
  • 2019-03 Special issue on “Advances on model-based clustering and classification”
  • 2019-03 Robust clustering for functional data based on trimming and constraints
  • 2019-03 Unifying data units and models in (co-)clustering
  • 2019-03 Studying crime trends in the USA over the years 2000–2012
  • 2019-03 Finite mixture of regression models for censored data based on scale mixtures of normal distributions
  • 2019-03 sARI: a soft agreement measure for class partitions incorporating assignment probabilities
  • 2019-03 Clustering space-time series: FSTAR as a flexible STAR approach
  • 2019-02-21 Bayesian shrinkage in mixture-of-experts models: identifying robust determinants of class membership
  • 2019-02-15 Exploration of the variability of variable selection based on distances between bootstrap sample results
  • 2019-02-12 Discriminant analysis for discrete variables derived from a tree-structured graphical model
  • 2018-12 Ensemble feature selection for high dimensional data: a new method and a comparative study
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