Spatial selection of sparse pivots for similarity search in metric spaces

Abstract

Similarity search is a necessary operation for applications dealing with unstructured data sources. In this paper we present a pivotbased method useful, not only to obtain a good pivot selection without specifying in advance the number of pivots, but also to obtain an insight in the complexity of the metric space. Sparse Spatial Selection (SSS) adapts itself to the dimensionality of the metric space, is dynamic, and it is suitable for secondary memory storage. In this paper we provide experimental results that confirm the advantages of the method with several metric spaces. Moreover, we explain how SSS can be easily parallelized. Finally, in this paper we conceptualize Nested Metric Spaces, and we prove that, in some applications areas, objects can be grouped in different clusters with different associated metric spaces, all of them nested into the general metric space that explains the distances among clusters. © Springer-Verlag Berlin Heidelberg 2007.