Center Selection Techniques for Metric Indexes

The metric spaces model formalizes the similarity search concept in nontraditional databases. The goal is to build an index designed to save distance computations when answering similarity queries later. A large class of algorithms to build the index are based on partitioning the space in zones as compact as possible. Each zone stores a representative point, called center, and a few extra data that allow to discard the entire zone at query time without measuring the actual distance between the elements of the zone and the query object. The way in which the centers are selected affects the performance of the algorithm. In this paper, we introduce two new center selection techniques for compact partition based indexes. These techniques were evaluated using the Geometric Near-neighbor Access Tree (GNAT). We experimentally showed that they achieve good performance.