Bayesian Clustering with Variable and Transformation Selections

@inproceedings{Liu2003BayesianCW,
  title={Bayesian Clustering with Variable and Transformation Selections},
  author={Jun S. Liu and Junni L. Zhang and Michael J. Palumbo and Charles E. Lawrence},
  year={2003}
}
SUMMARY The clustering problem has attracted much attention from both statisticians and computer scientists in the past fifty years. Methods such as hierarchical clustering and the K-means method are convenient and competitive first choices off the shelf for the scientist. Gaussian mixture modeling is another popular but computationally expensive clustering strategy, especially when the data is of high-dimensional. We propose to first conduct a principal component analysis (PCA) or… CONTINUE READING

Figures from this paper.

Citations

Publications citing this paper.
SHOWING 1-10 OF 62 CITATIONS

Penalized model-based clustering with cluster-specific diagonal covariance matrices and grouped variables.

  • Electronic journal of statistics
  • 2008
VIEW 6 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

Modeling Multivariate Biomedical Data

VIEW 6 EXCERPTS
CITES BACKGROUND & METHODS
HIGHLY INFLUENCED

SAR Image Denoising via Clustering-Based Principal Component Analysis

  • IEEE Transactions on Geoscience and Remote Sensing
  • 2014
VIEW 4 EXCERPTS
CITES METHODS
HIGHLY INFLUENCED

Sparse Bayesian hierarchical modeling of high-dimensional clustering problems

  • J. Multivariate Analysis
  • 2009
VIEW 3 EXCERPTS
CITES METHODS
HIGHLY INFLUENCED

FILTER CITATIONS BY YEAR

2003
2019

CITATION STATISTICS

  • 6 Highly Influenced Citations

References

Publications referenced by this paper.
SHOWING 1-10 OF 55 REFERENCES

An Analysis of Transformations

VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL

Finite Mixture Models

  • Wiley Series in Probability and Statistics
  • 2000
VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL