Title: Explore clustering interactively using R and GGobi
Description: Visualise clustering algorithms with GGobi.  Contains both general code for visualising clustering results and specific visualisations for model-based, hierarchical and SOM clustering. See http://had.co.nz/clusterfly for more information.
Depends: rggobi, ggplot2, e1071, cluster
Packaged: Mon Jun 25 12:55:59 2007; hadley

Packaged: Thu Jun 28 09:51:03 2007; hadley

Description: See http://had.co.nz/reshape for more information.
Packaged: Sun Jun 17 18:15:37 2007; hadley

Description: Visualise clustering algorithms with GGobi.  Contains both
general code for visualising clustering results and specific
visualisations for model-based, hierarchical and SOM
Collate: 'clusterfly.r' 'data.r' 'ellipsoid.r' 'extract.r' 'ggplot.r'
'hclust.r' 'hulls.r' 'model-based.r' 'som-iteration.r' 'som.r'

visualisations for model-based, hierarchical and SOM clustering.

Title: A hierarchical clustering method based on genetic algorithms
Author: Jose A. Castellanos-Garzon <jantonio_cu@ieee.org>, Fernando
Maintainer: Jose A. Castellanos-Garzon <jantonio_cu@ieee.org>
Description: This package develops a genetic algorithm to build
hierarchical clusterings from a dataset. It additionally
provides a set of functions applied to cluster analysis.

Title: Clustering Genotypes in Haplotypes
Author: Gaston Quero <gastonquero@gmail.com> and Sebastian Simondi <sebastian.simondi@gmail.com>  , with
contributions from Victoria Bonnecarrere and Lucia Gutierrez
Description: One haplotype is a combination of SNP
(Single Nucleotide Polymorphisms) within the QTL (Quantitative Trait Loci).
clusterhap groups together all individuals of a population with the same haplotype.
Each group contains individual with the same allele in each SNP,
whether or not missing data. Thus, clusterhap groups individuals,
that to be imputed, have a non-zero probability of having the same alleles
in the entire sequence of SNP's. Moreover, clusterhap calculates such

Title: Optimal Distance-Based Clustering for Multidimensional Data with
Authors@R: c(person("Tibor", "Szkaliczki", role = c("aut", "cre"),
Description: A dynamic programming algorithm for optimal clustering multidimensional data with sequential constraint. The algorithm minimizes the sum of squares of within-cluster distances. The sequential constraint allows only subsequent items of the input data to form a cluster. The sequential constraint is typically required in clustering data streams or items with time stamps such as video frames, GPS signals of a vehicle, movement data of a person, e-pen data, etc. The algorithm represents an extension of Ckmeans.1d.dp to multiple dimensional spaces. Similarly to the one-dimensional case, the algorithm guarantees optimality and repeatability of clustering. Method clustering.sc.dp can find the optimal clustering if the number of clusters is known. Otherwise, methods findwithinss.sc.dp and backtracking.sc.dp can be used.