R: fpc package overview

fpc-package {fpc} | R Documentation

## fpc package overview

### Description

Here is a list of the main functions in package fpc. Most other functions are auxiliary functions for these.

### Clustering methods

dbscan

  Computes DBSCAN density based clustering as introduced in Ester et al. (1996).

fixmahal

  Mahalanobis Fixed Point Clustering, Hennig and Christlieb (2002), Hennig (2005).

fixreg

  Regression Fixed Point Clustering, Hennig (2003).

flexmixedruns

  This fits a latent class model to data with mixed type continuous/nominal variables. Actually it calls a method for `flexmix`.

mergenormals

  Clustering by merging components of a Gaussian mixture, see Hennig (2010).

regmix

  ML-fit of a mixture of linear regression models, see DeSarbo and Cron (1988).

### Cluster validity indexes and estimation of the number of clusters

cluster.stats

  This computes several cluster validity statistics from a clustering and a dissimilarity matrix including the Calinski-Harabasz index, the adjusted Rand index and other statistics explained in Gordon (1999) as well as several characterising measures such as average between cluster and within cluster dissimilarity and separation. See also `calinhara`, `dudahart2` for specific indexes.

prediction.strength

  Estimates the number of clusters by computing the prediction strength of a clustering of a dataset into different numbers of components for various clustering methods, see Tibshirani and Walther (2005). In fact, this is more flexible than what is in the original paper, because it can use point classification schemes that work better with clustering methods other than k-means.

nselectboot

  Estimates the number of clusters by bootstrap stability selection, see Fang and Wang (2012). This is quite flexible regarding clustering methods and point classification schemes and also allows for dissimilarity data.

### Cluster visualisation and validation

clucols

  Sets of colours and symbols useful for cluster plotting.

clusterboot

  Cluster-wise stability assessment of a clustering. Clusterings are performed on resampled data to see for every cluster of the original dataset how well this is reproduced. See Hennig (2007) for details.

cluster.varstats

  Extracts variable-wise information for every cluster in order to help with cluster interpretation.

plotcluster

  Visualisation of a clustering or grouping in data by various linear projection methods that optimise the separation between clusters, or between a single cluster and the rest of the data according to Hennig (2004) including classical methods such as discriminant coordinates. This calls the function `discrproj`, which is a bit more flexible but doesn't produce a plot itself.

ridgeline.diagnosis

  Plots and diagnostics for assessing modality of Gaussian mixtures, see Ray and Lindsay (2005).

weightplots

  Plots to diagnose component separation in Gaussian mixtures, see Hennig (2010).

localshape

  Local shape matrix, can be used for finding clusters in connection with function `ics` in package `ICS`, see Hennig's discussion and rejoinder of Tyler et al. (2009).

### Useful wrapper functions for clustering methods

kmeansCBI

  This and other "CBI"-functions (see the `kmeansCBI`-help page) are unified wrappers for various clustering methods in R that may be useful because they do in one step for what you normally may need to do a bit more in R (for example fitting a Gaussian mixture with noise component in package mclust).

kmeansruns

  This calls `kmeans` for the k-means clustering method and includes estimation of the number of clusters and finding an optimal solution from several starting points.

pamk

  This calls `pam` and `clara` for the partitioning around medoids clustering method (Kaufman and Rouseeuw, 1990) and includes two different ways of estimating the number of clusters.

### Author(s)

Christian Hennig c.hennig@ucl.ac.uk http://www.homepages.ucl.ac.uk/~ucakche/

### References

DeSarbo, W. S. and Cron, W. L. (1988) A maximum likelihood methodology for clusterwise linear regression, Journal of Classification 5, 249-282.

Ester, M., Kriegel, H.-P., Sander, J. and Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96).

Fang, Y. and Wang, J. (2012) Selection of the number of clusters via the bootstrap method. Computational Statistics and Data Analysis, 56, 468-477.

Gordon, A. D. (1999) Classification, 2nd ed. Chapman and Hall.

Hennig, C. (2003) Clusters, outliers and regression: fixed point clusters, Journal of Multivariate Analysis 86, 183-212.

Hennig, C. (2004) Asymmetric linear dimension reduction for classification. Journal of Computational and Graphical Statistics, 13, 930-945 .

Hennig, C. (2005) Fuzzy and Crisp Mahalanobis Fixed Point Clusters, in Baier, D., Decker, R., and Schmidt-Thieme, L. (eds.): Data Analysis and Decision Support. Springer, Heidelberg, 47-56, http://www.homepages.ucl.ac.uk/~ucakche/papers/fuzzyfix.ps

Hennig, C. (2007) Cluster-wise assessment of cluster stability. Computational Statistics and Data Analysis, 52, 258-271.

Hennig, C. (2010) Methods for merging Gaussian mixture components, Advances in Data Analysis and Classification, 4, 3-34.

Hennig, C. and Christlieb, N. (2002) Validating visual clusters in large datasets: Fixed point clusters of spectral features, Computational Statistics and Data Analysis 40, 723-739.

Kaufman, L. and Rousseeuw, P.J. (1990). "Finding Groups in Data: An Introduction to Cluster Analysis". Wiley, New York.

Ray, S. and Lindsay, B. G. (2005) The Topography of Multivariate Normal Mixtures, Annals of Statistics, 33, 2042-2065.

Tibshirani, R. and Walther, G. (2005) Cluster Validation by Prediction Strength, Journal of Computational and Graphical Statistics, 14, 511-528.

[Package fpc version 2.1-10 Index]