Hyperspectral images consisting of large number of spectral bands suffer from limitations like High data redundancy, curse of dimensionality (insufficient training samples), and high computational complexity. Therefore, dimensionality reduction & band-selection has become a common practice in the field of hyperspectral image processing. Graph-based band selection is a well-known technique which is based on spectral clustering on similarity matrix to select the optimal band set. Thus, choice of similarity/ affinity matrix is a vital decision in these methods. We have conducted a comparison of some well-known affinity matrices used in spectral clustering to divide graph to smaller sub-graphs (indicating subsets of bands). Comparison was done using various types of metrics like ACC, AIE and ARE.