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Testing for Differences Among Discrete Distributions: An Application of Model-Based Clustering
Differences Discrete Distributions Model-Based Clustering
2009/9/17
Testing for Differences Among Discrete Distributions: An Application of Model-Based Clustering。
Penalized model-based clustering with cluster-specific diagonal covariance matrices and grouped variables
EM algorithm High-dimension but low-sample size L1 penalization Microarray gene expression Mixture model Penalized likelihood
2009/9/16
Clustering analysis is one of the most widely used statistical tools in many emerging areas such as microarray data analysis. For microarray and other high-dimensional data, the presence of many noise...
Random Clustering Based on the Conditional Inverse Gaussian-Poisson Distribution
disclosure risk frequencies of frequencies size index species abundance superpopulation
2009/3/10
The present article describes a Conditional Inverse Gaussian-Poisson (CIGP) distribution, obtained by conditioning an inverse Gaussian-Poisson population model on its total frequency. This CIGP distri...
Correspondence analysis and two-way clustering
block clustering selecting number of axes data visualization
2009/2/23
Correspondence analysis followed by clustering of both rows and columns of a data matrix is proposed as an approach to two-way clustering. The novelty of this contribution consists of: i) proposing a ...
Directional Clustering Tests Based on Nearest Neighbor Contingency Tables
Association clustering complete spatial randomness independence random labeling spatialpattern
2010/3/18
Spatial interaction between two or more classes or species has important implications in various fields and causes multivariate patterns such as segregation or association. Segregation occurs when mem...
Persistent Clustering and a Theorem of J. Kleinberg
Clustering hierarchical clustering persistent topology categories functoriality Gromov-Hausdorff distance
2010/4/30
We construct a framework for studying clustering algorithms, which includes two key ideas:
persistence and functoriality. The first encodes the idea that the output of a clustering scheme should carr...