Principle Components Analysis (PCA) and Factor Analysis are data reduction techniques. The objective to to reduce large numbers of variables to a smaller number of "factors", or underlying constructs that explain the variation. For example, you have data on gripping strength of each hand, lower and upper arm strength. You have the same for different measures of leg strength. PCA or Factor Analysis might reduce this to two constructs called upper body strength and lower body strength. Both methods focus on the Independent Variables.

Cluster Analysis may be run on raw data, scores from Factor Analysis, or on variables. For the first two, CA will find clusters of entities (think market segments) that behave in a similar fashion. For the latter, CA will find variables that measure similar responses. This is particularly useful to identify survey questions that are asking the same thing.