fpc: Flexible procedures for clustering. Various methods for clustering and cluster validation. Fixed point clustering. Linear regression clustering. Clustering by merging Gaussian mixture components. Symmetric and asymmetric discriminant projections for visualisation of the separation of groupings. Cluster validation statistics for distance based clustering including corrected Rand index. Cluster-wise cluster stability assessment. Methods for estimation of the number of clusters: Calinski-Harabasz, Tibshirani and Walther’s prediction strength, Fang and Wang’s bootstrap stability. Gaussian/multinomial mixture fitting for mixed continuous/categorical variables. Variable-wise statistics for cluster interpretation. DBSCAN clustering. Interface functions for many clustering methods implemented in R, including estimating the number of clusters with kmeans, pam and clara. Modality diagnosis for Gaussian mixtures. For an overview see package?fpc.
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References in zbMATH (referenced in 5 articles )
Showing results 1 to 5 of 5.
- Roy Chowdhury, Niladri; Cook, Dianne; Hofmann, Heike; Majumder, Mahbubul; Lee, Eun-Kyung; Toth, Amy L.: Using visual statistical inference to better understand random class separations in high dimension, low sample size data (2015)
- Galimberti, Giuliano; Montanari, Angela: Discussion of “Model-based clustering and classification with non-normal mixture distributions” by S. X. Lee and G. J. McLachlan (2013)
- Newell, Mark A.; Cook, Dianne; Hofmann, Heike; Jannink, Jean-Luc: An algorithm for deciding the number of clusters and validation using simulated data with application to exploring crop population structure (2013)
- Schlittgen, Rainer: Regression analyses with R (2013)
- Zhao, Yanchang: R and data mining. Examples and case studies (2013)