Principal approaches:
ØHierarchical clustering (kdb trees, CART, gene
shaving)
ØK-means
clustering
ØSelf
organizing (Kohonen) maps
ØVector
support machines
ØGene
Filtering via Multiobjective Optimization
ØIndependent
Component Analysis (ICA)
Validation approaches:
ØSignificance
analysis of microarrays (SAM)
ØBootstrapping
cluster analysis
ØLeave-one-out
cross-validation
ØReplication (additional gene chip experiments,
quantitative PCR)
Clustering and filtering methods