2. The method of claim 1, further comprising the steps of: (a) generating cluster boundaries, which correspond to a zeroth-order approximation to a geometry of said clusters; (b) approximating the geometry of each of the clusters by means of a minimum bounding box and generating a first-order approximation to the geometry of each cluster; (c) partitioning the bounding box into 2 k hyper rectangles, wherein said partitioning is at a midpoint of each dimension; (d) retaining only those hyper rectangles that contain data points and generating a second-order approximation to the geometry of the cluster; and (e) repeating said steps (c) and (d) for each retained hyper rectangle to generate successively the third-, fourth-, . . . , n-th approximations to the geometry of the cluster.
1. A computerized method of representing multidimensional data, comprising the steps of: a) partitioning the multidimensional data into one or more clusters; b) generating and storing clustering information for said one or more clusters; c) generating one or more reduced dimensionality clusters and dimensionality reduction information for said one or more clusters; d) storing the dimensionality reduction information; e) creating a hierarchy of reduced dimensionality clusters by recursively applying said steps a) through d); and f) generating and storing one or more low-dimensional idexes for clusters at a lowest level of said hierarchy.