I just read a article about the comparison between PCA and hierarchical clustering, but I cannot find the strengths and weakness of clustering compared Principal Component Analysis, what about other clustering algorithm? Could anyone provide some details about this?
PCA - creates a low-dimensional representation of the samples from a data set which is optimal in the sense that it contains as much of the variance in the original data set as is possible. PCA also provides a variable representation that is directly connected to the sample representation, and which allows the user to visually find variables that are characteristic for specific sample groups.
Hierarchical clustering - builds a tree-like structure (a dendrogram) where the leaves are the individual objects (samples or variables) and the algorithm successively pairs together objects showing the highest degree of similarity.