Isomap

Matrix containing the distances of the data

Inputdistances

number of k nearest neighbors, if the data is fragmented choose an higher k

Number of dimensions in the output space, default = 2

OutputDimension

Default: FALSE, If TRUE: Plots the projection as a 2d visualization. 
	 If OutputDimension &gt; 2 only the first two dimensions will be shown.

PlotIt

Optional and only relevant if PlotIt=TRUE. Numeric vector, given Classification in numbers: every element is the cluster number of a certain corresponding element of data.

Isomap procetion as introduced in 2000 by Tenenbaum, de Silva and Langford
Even with a manifold structure, the sampling must be even and dense so that dissimilarities along a manifold are shorter than across the folds. If data do not have such a manifold structure, the results are very sensitive to parameter values.

A clustering approach for every projection method based on the generalized  U*-matrix visualization of a topographic map is made available here. The number of clusters and the cluster structure can be estimated by counting the valleys in a topographic map. If the number of clusters and the clustering method are chosen correctly, then the clusters will be well separated by mountains in the visualization. Most projection methods are wrappers for already available methods in R. However, the neighbor retrieval visualizer (NeRV) is based on C++ source code of the 'dredviz' software package and the Curvilinear Component Analysis (CCA) is translated from 'MATLAB' ('SOM Toolbox' 2.0) to R.

Isomap: Isomap projection method

Description

Usage

Arguments

Value