7.1 Module Clusfind

This module comprises the following six algorithms:

Algorithm	Description of the Algorithm	Data Input
PAM	Partitioning around medoids A crisp partitioning method for smaller data sets	n ´ n dissimilarity matrix n ´ p data matrix ( interval scale variables)
CLARA	Clustering large applications Partitioning method for large data sets ( More than 100 objects)	n ´ n dissimilarity matrix n ´ p data matrix ( interval scale variables)
FANNY	Fuzzy analysis Fuzzy partitioning method based on the concept of membership	n ´ n dissimilarity matrix n ´ p data matrix ( interval scale variables)
AGNES	Agglomerative nesting Agglomerative hierarchical clustering method	n ´ n dissimilarity matrix n ´ p data matrix ( interval scale variables)
DIANA	Divisive Analysis Divisive hierarchical clustering method	n ´ n dissimilarity matrix n ´ p data matrix ( interval scale variables)
MONA	Monothetic Analysis Divisive hierarchical clustering method	n ´ p data matrix (Binary variables)

If the input matrix is a correlation matrix with elements r_ij , the elements of the dissimilarity matrix are calculated, using one of the two formulae:

SIGN	d ij = (1- r ij)/2
ABSOLUTE	d ij = ÷ 1- r ij÷