Clustering
Concepts
These components perform clustering, they produce "homogenous" group by optimization of some criteria. They are also known as unsupervised methods.
Attributes status
"Input" attributes only, they are generally continuous.Clustering components
Component  Description  Parameters  Note 
KMeans 
KMeans  Forgy and Mc Queen algorithms. Several trials are performed.
 T. Hastie, R. Tibshirani, J. Friedman, "The elements of statistical learning. Data Mining,
inference and predictions.", Springer, pp.461463, 2001. 
 Number of clusters  Number of iterations  Numberof trials  Data standardization  Average computation during optimization process 

Kohonen's SOM 
Kohonen's Self Organization Map.
 T. Kohonen, "Selforganization and associative memory", SpringerVerlag, 1988. 
 Map : number of row  Map : numberof columns  Data standardization  Learning rate 

LVQ 
Kohonen's Learning Vector Quantizers, a "supervised" clustering algorithm.
 T. Kohonen, "Selforganization and associative memory", SpringerVerlag, 1988. 
 Number of cluster per class  Learning rate  Number of iterations  Data standardization 
"Target" discrete attribute must be defined. 
HAC 
Hierachical agglomerative clustering.
The tree is built into two steps : This method allows to apply HAC on dataset with many examples. How to set the number of lowlevel clusters ? It must be lower than the size of the dataset, a good compromise seems to be 15  20.
M.A. Wong, "A hybrid clustering method for identifying high density clusters", JASA, 77, pp.841847, 1982. 
 Clusters detection strategy  Number of clusters  Standardization or not of attributes for distance evaluation  Discrete TARGET must be defined, it comes from first clustering component such as KMEANS or SOM. 