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Clustering via Nonparametric Density Estimation: The R Package pdfCluster

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  • Azzalini, Adelchi
  • Menardi, Giovanna

Abstract

The R package pdfCluster performs cluster analysis based on a nonparametric estimate of the density of the observed variables. Functions are provided to encompass the whole process of clustering, from kernel density estimation, to clustering itself and subsequent graphical diagnostics. After summarizing the main aspects of the methodology, we describe the features and the usage of the package, and finally illustrate its application with the aid of two data sets.

Suggested Citation

  • Azzalini, Adelchi & Menardi, Giovanna, 2014. "Clustering via Nonparametric Density Estimation: The R Package pdfCluster," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 57(i11).
    • Handle: RePEc:jss:jstsof:v:057:i11
    DOI: http://hdl.handle.net/10.18637/jss.v057.i11
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    Citations

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    Cited by:

    1. Giovanna Menardi, 2016. "A Review on Modal Clustering," International Statistical Review, International Statistical Institute, vol. 84(3), pages 413-433, December.
    2. Abby Flynt & Nema Dean, 2016. "A Survey of Popular R Packages for Cluster Analysis," Journal of Educational and Behavioral Statistics, , vol. 41(2), pages 205-225, April.
    3. Zicheng Wang & Yunong Xia & Lauren Mills & Athanasios N. Nikolakopoulos & Nicole Maeser & Scott M. Dehm & Jason M. Sheltzer & Ruping Sun, 2024. "Evolving copy number gains promote tumor expansion and bolster mutational diversification," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    4. Yi Jin & Yulin He & Defa Huang, 2021. "An Improved Variable Kernel Density Estimator Based on L 2 Regularization," Mathematics, MDPI, vol. 9(16), pages 1-12, August.
    5. Stefano Tonellato, 2019. "Bayesian nonparametric clustering as a community detection problem," Working Papers 2019: 20, Department of Economics, University of Venice "Ca' Foscari".
    6. Mendez-Guerra, Carlos, 2017. "Convergence Clubs Beyond GDP: A Non-Parametric Density Approach," MPRA Paper 82048, University Library of Munich, Germany.
    7. Adelchi Azzalini & Giovanna Menardi, 2016. "Density-based clustering with non-continuous data," Computational Statistics, Springer, vol. 31(2), pages 771-798, June.
    8. Jingting Xu & Hong Hu & Yang Dai, 2016. "LMethyR-SVM: Predict Human Enhancers Using Low Methylated Regions based on Weighted Support Vector Machines," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-18, September.
    9. Mahdi Salehi & Andriette Bekker & Mohammad Arashi, 2023. "A Semi-parametric Density Estimation with Application in Clustering," Journal of Classification, Springer;The Classification Society, vol. 40(1), pages 52-78, April.
    10. Hui Ye & Anthony Bellotti, 2019. "Modelling Recovery Rates for Non-Performing Loans," Risks, MDPI, vol. 7(1), pages 1-17, February.
    11. Tonellato, Stefano F., 2020. "Bayesian nonparametric clustering as a community detection problem," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).

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