Linear Clustering Of Objects With Multiple Attributes

Abstract

There is often a need to map a multi-dimensional space on to a one-dimensional space. For example, this kind of mapping has been proposed to permit the use of one-dimensional indexing techniques to a multi-dimensional index space such as in a spatial database. This kind of mapping is also of value in assigning physical storage, such as assigning buckets to records that have been indexed on multiple attributes, to minimize the disk access effort. In this paper, we discuss what the desired properties of such a mapping are, and evaluate, through analysis and simulation, several mappings that have been proposed in the past. We present a mapping based on Hilbert's space-filling curve, which out-performs previously proposed mappings on average over a variety of different operating conditions.