Several other clustering methods have also been considered but have not been used in our experimental comparison. Agglomerative models (single link, average link, complete link) [DH73] are computationally expensive (at least ) and often result in highly skewed trees, indicating domination by one very large cluster. Mixture models [Fuk72] such as AUTOCLASS [CS96] are also popular but are limited by problems of parameter estimation for high-dimensional data. Clustering algorithms from the data mining community (e.g., CLARANS, DBSCAN, BIRCH, CLIQUE, CURE, WAVECLUSTER [RS99,HKT01]) have been omitted since they are mostly scalable versions designed for low-dimensional data. Partitioning approaches based on principal directions have not been shown here since they perform comparably to hierarchical agglomerative clustering [BGG$^+$99]. Other graph partitioning approaches such as spectral bisectioning [HL95] are not included since they are already represented by the multi-level partitioner METIS.