R package pdfCluster: Cluster analysis via nonparametric density estimation. The package performs cluster analysis via nonparametric density estimation. Operationally, the kernel method is used throughout to estimate the density. Diagnostics methods for evaluating the quality of the clustering are available. The package includes also a routine to estimate the probability density function obtained by the kernel method, given a set of data with arbitrary dimensions.
Keywords for this software
References in zbMATH (referenced in 12 articles , 1 standard article )
Showing results 1 to 12 of 12.
- Menardi, Giovanna: Nonparametric clustering for image segmentation (2020)
- Alexander Foss; Marianthi Markatou: kamila: Clustering Mixed-Type Data in R and Hadoop (2018) not zbMATH
- Dixit, Anand; Roy, Vivekananda: MCMC diagnostics for higher dimensions using Kullback Leibler divergence (2017)
- Lin, Lin; Li, Jia: Clustering with hidden Markov model on variable blocks (2017)
- Migliorati, Sonia; Ongaro, Andrea; Monti, Gianna S.: A structured Dirichlet mixture model for compositional data: inferential and applicative issues (2017)
- Azzalini, Adelchi; Menardi, Giovanna: Density-based clustering with non-continuous data (2016)
- Foss, Alex; Markatou, Marianthi; Ray, Bonnie; Heching, Aliza: A semiparametric method for clustering mixed data (2016)
- Scrucca, Luca: Identifying connected components in Gaussian finite mixture models for clustering (2016)
- Wang, Xuxu; Wang, Yong: Nonparametric multivariate density estimation using mixtures (2015)
- Menardi, Giovanna; Azzalini, Adelchi: An advancement in clustering via nonparametric density estimation (2014)
- Adelchi Azzalini, Giovanna Menardi: Clustering Via Nonparametric Density Estimation: the R Package pdfCluster (2013) arXiv
- Menardi, Giovanna; Torelli, Nicola: Reducing data dimension for cluster detection (2013)