Function reference • T4cluster

[1] Clustering with Euclidean Data
`dpmeans()`	DP-Means Clustering
`gmm()`	Finite Gaussian Mixture Model
`gmm03F()`	Ensemble of Gaussian Mixtures with Random Projection
`gmm11R()`	Regularized GMM by Ruan et al. (2011)
`gmm16G()`	Weighted GMM by Gebru et al. (2016)
`kmeans()`	K-Means Clustering
`kmeans18B()`	K-Means Clustering with Lightweight Coreset
`kmeanspp()`	K-Means++ Clustering
`sc05Z()`	Spectral Clustering by Zelnik-Manor and Perona (2005)
`sc09G()`	Spectral Clustering by Gu and Wang (2009)
`sc10Z()`	Spectral Clustering by Zhang et al. (2010)
`sc11Y()`	Spectral Clustering by Yang et al. (2011)
`sc12L()`	Spectral Clustering by Li and Guo (2012)
`scNJW()`	Spectral Clustering by Ng, Jordan, and Weiss (2002)
`scSM()`	Spectral Clustering by Shi and Malik (2000)
`scUL()`	Spectral Clustering with Unnormalized Laplacian
[2] Clustering with Functional Data
`funhclust()`	Functional Hierarchical Clustering
`funkmeans03A()`	Functional K-Means Clustering by Abraham et al. (2003)
[3] Clustering with Spherical Data
`gskmeans()`	Geodesic Spherical K-Means
`spkmeans()`	Spherical K-Means Clustering
[4] Clustering with Empirical Distributions
`ephclust()`	Hierarchical Agglomerative Clustering for Empirical Distributions
[5] Subspace Clustering
`EKSS()`	Ensembles of K-Subspaces
`LRR()`	Low-Rank Representation
`LRSC()`	Low-Rank Subspace Clustering
`LSR()`	Least Squares Regression
`MSM()`	Bayesian Mixture of Subspaces of Different Dimensions
`SSC()`	Sparse Subspace Clustering
`SSQP()`	Subspace Segmentation via Quadratic Programming
`predict(<MSM>)`	S3 method to predict class label of new data with 'MSM' object
[6] Measures : Clustering Validity/Quality Index
`quality.CH()`	(+) CH index
`quality.sil()`	(+) Silhouette Index
[7] Measures : Comparing Two Clusterings
`compare.adjrand()`	(+) Adjusted Rand Index
`compare.rand()`	(+) Rand Index
[8] Learning with Multiple Clusterings
`pcm()`	Compute Pairwise Co-occurrence Matrix
`psm()`	Compute Posterior Similarity Matrix
[9] Data : Loaded and Generators
`gen3S()`	Generate from Three 5-dimensional Subspaces in 200-dimensional space.
`genDONUTS()`	Generate Nested Donuts
`genLP()`	Generate Line and Plane Example with Fixed Number of Components
`genSMILEY()`	Generate SMILEY Data
`household`	Load 'household' data

Reference

[1] Clustering with Euclidean Data

[2] Clustering with Functional Data

[3] Clustering with Spherical Data

[4] Clustering with Empirical Distributions

[5] Subspace Clustering

[6] Measures : Clustering Validity/Quality Index

[7] Measures : Comparing Two Clusterings

[8] Learning with Multiple Clusterings

[9] Data : Loaded and Generators