Abstract: In a method for clustering high-dimensional data, the high-dimensional data is collected in two hierarchical data structures. The first data structure, called O-Tree, stores the data in data sets designed for representing clustering information. The second data structure, called R-Tree, is designed for indexing the data set in reduced dimensionality. R-Tree is a variant of O-Tree, where the dimensionality of O-Tree is reduced using singular value decomposition to produce R-Tree. The user specifies requirements for the clustering, and clusters of the high-dimensional data are selected from the two hierarchical data structures in accordance with the specified user requirements.