Clustering data and searching optimal cutoff employing VIF criterion 1.0

Performs hierarchical clustering of data using specified method andseraches for optimal cutoff empoying VIF criterion suggested in "Okada Y. et al - Detection of Cluster Boundary in Microarray Data by Reference to MIPS Functional Catalogue Database (2001)".Namely, it searches cutoff where groups are independent. The techinque uses an econometric approach of verifying that variables inmultiple regression are linearly independent: if all the diagonalelements of inverse correlation matrix of data are less than VIF (asrule of thumb VIF=10).Searching procedure is the variaition of bisection method, so it'scomplexity is log(n) at most. At each iteration it chooses one itemfrom every clusters, constructs correlation matrix of these items andlook at diagonal element of its inverse.

Requirements:	No special requirements
Platforms:	Matlab
Keyword:	Approach,  Correlation,  Diagonal,  Elements,  Inverse,  Linearly,  Matrix,  Thumb,  Verifying,  Vif
Users rating:	0/10