klrank

Data

pData

<p>Sets the 'effective number of neighbors' used to control the width of the Gaussian</p>

NeighborhoodSize

<p>Compares the projection in pData with the original data in Data and calculates the rank-based smoothed recall and precision.</p>

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.

Michael Thrun

ProjectionBasedClustering

Projection Based Clustering

Florian Lerch

Felix Pape

Kristian Nybo

Jarkko Venna

klrank function

Computes rank-based smoothed precision/recall — klrank

Computes rank-based smoothed precision/recall

klrank: Computes rank-based smoothed precision/recall

Description

Usage

Arguments

Value

References