Most clustering methods search for one optimal partitioning of the data. Often it is better to search for many different clusterings of the data and present the user with a means of efficiently navigating between them. We present two algorithms for generating many alternate clusterings: Sample-and-Merge and Component Reweighting. We then use clustering at a meta level to organize these different base-level clusterings. This {\em MetaClustering} partitions the base-level clusterings into groups of similar clusterings. We demonstrate MetaClustering on a synthetic data set, and on a real protein data set. The results show that the algorithms are effective at generating qualitatively different clusterings, and at organizing these clusterings so that similar ones are grouped together.