Gero Szepannek [aut, cre], Rabea Aschenbruck [aut]

Easy Interface for Clustering a Set of Documents and Exploring
Group- Based Patterns
Provides an interface to perform cluster analysis on a corpus of text. Interfaces to
Quanteda to assemble text corpuses easily. Deviationalizes text vectors prior to clustering
using technique described by Sherin (Sherin, B. [2013]. A computational study of commonsense science:
An exploration in the automated analysis of clinical interview data. Journal of the Learning Sciences,
22(4), 600-638. Chicago. http://dx.doi.org/10.1080/10508406.2013.836654). Uses cosine similarity as distance
metric for two stage clustering process, involving Ward's algorithm hierarchical agglomerative clustering,
and k-means clustering. Selects optimal number of clusters to maximize "variance explained" by clusters,
adjusted by the number of clusters. Provides plotted output of clustering results as well as printed output.
Assesses "model fit" of clustering solution to a set of preexisting groups in dataset.

Provides an interface to perform cluster analysis on a corpus of
text. Interfaces to Quanteda to assemble text corpuses easily. Deviationalizes
text vectors prior to clustering using technique described by Sherin (Sherin,
B. [2013]. A computational study of commonsense science: An exploration in the
automated analysis of clinical interview data. Journal of the Learning Sciences,
22(4), 600-638. Chicago. <doi:10.1080/10508406.2013.836654>). Uses
cosine similarity as distance metric for two stage clustering process, involving
Ward's algorithm hierarchical agglomerative clustering, and k-means clustering.
Selects optimal number of clusters to maximize "variance explained" by clusters,
adjusted by the number of clusters. Provides plotted output of clustering
results as well as printed output. Assesses "model fit" of clustering solution
to a set of preexisting groups in dataset.

GUI for clustering data with spatial information
R (>= 2.2.0), mvoutlier, cluster, randomForest, tcltk, e1071, rpart, class, grid, mclust, fpc, flexclust, vegan, clue, mvpart, lattice, kernlab, Rcmdr
This package can be used for clustering data with spatial information.