Multivariate clustering & classification
- Normal mixture models
- Several codes are available that characterize multivariate
datasets as mixtures of Gaussian populations via likelihood methods, often using
Bayesian principles. They include:
EMMIX by G.
McLachlan,
MCLUST
by C. Fraley and A. Raftery, and
AutoClass C
by P. Cheeseman, and
Snob by D. Dowe.
P
- Dysect
- Clustering algorithm based on dynamic altering of hierarchies.
(P)
- Fast Algorithm for
Classification Trees"
- Tree-structures classification similar to CART. (P)
- Machine Learning Library in
C++ (MLC++)
- Data mining and multivariate classification package including
data manipulation, variety of categorizers (on attributes, thresholds, nearest
neighbor, perceptron, decision tree ), induction algorithms, and visualization
tools of data and trees. (P)
- R Package
- Package in Pascal developed for ecological spatio-temporal multivariate datasets
based on monograph by L. & P. Legendre (1983). Functionalities include
autocorrelation using correlograms (Moran's I and Geary's c indices),
hierarchical agglomerative clustering, k-means clustering, chronological clustering
for multivariate time series, analysis of variance, geometrical connectors,
(nearest neighbor, Gabriel's connection, Delaunay triangulation), Mantel's two-sample
statistic, multidimensional scaling by principal coordinates analysis, univariate
periodogram. (P)
- Cluster
- Library of several dozen subroutines from NIST for multivariate clustering
algorithm from 1975 monobraph by J. A. Hartigan.
- Multivariate
data analysis software
- Collection of subroutines for principal components analysis, partitioning,
hierarchical clustering. discriminant analyses (linear, multiple, k-nearest
neighbors), correspondence analysis, multidimensional scaling, Sammon mapping,
Kohonen self-organizing feature map.
- Cluster analysis
- Six programs computing dissimilarities, partitioning using medoids,
k-medoid clustering, fuzzy clustering, agglomerative and divisive hierarchical
clustering, clustering of binary data.
- CLUSBAS
- Average-linkage hierarchical clustering.
- MClust
- Agglomerative hierarchical clustering with a variety of cluster shape
criteria.
- Random Forest
- Advancement on CART that separates objects into
classes under a wide range of circumstances: unknown number
of classes, non-Gaussian shapes, redundant variables. Includes
density estimation, variable importance, and measure of outliers.
From Leo Breiman, UC Berkeley.
- Hierarchical clustering
- Algorithm for agglomerative clustering using various criteria (Ward's
minimum variance, single linkage, average linkage, complete linkage, McQuitty's
method, median method, centroid method).
- AS 15 ,
- Algorithm for single-linkage and minimum intra-cluster variance
clustering.
- AS 58
- Algorithm for single-linkage and minimum intra-cluster variance
clustering.
- k-means clustering ,
- k-means clustering minimizing intra-cluster variance.
- Classification Society of North
America (CSNA)
- Metasite with many links to classification meetings, journals, discussion
groups, commercial and on-line software.
-
Software for clustering and multivariate analysis
- Metasite with discriptions of on-line programs and packages.
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