We propose a novel approach to solving the approximate k-nearest neighbor search
problem in metric spaces. The search structure is based on a navigable small world graph …

We introduce a new probabilistic proximity search algorithm for range and A"-nearest
neighbor (A"-NN) searching in both coordinate and metric spaces. Although there exist …

We present a new similarity search library and discuss a variety of design and performance
issues related to its development. We adopt a position that engineering is equally important …

The primary goal of this article is to survey state-of-the-art indexing methods for approximate
dictionary searching. To improve understanding of the field, we introduce a taxonomy that …

The triangular inequality is a defining property of a metric space, while the stronger
ultrametric inequality is a defining property of an ultrametric space. Ultrametric distance is …

Feature selection is a popular data pre-processing step. The aim is to remove some of the
features in a data set with minimum information loss, leading to a number of benefits …

We review the major paradigms for approximate similarity queries and propose a
classification schema that easily allows existing approaches to be compared along several …

Clustering evaluation plays an important role in unsupervised learning systems, as it is often
necessary to automatically quantify the quality of generated cluster configurations. This is …

An exhaustive search of all classes in pattern recognition methods cannot be implemented
in real-time, if the database contains a large number of classes. In this paper we introduce a …

Searching by proximity has been a source of puzzling behaviors and counter-intuitive
findings for well established algorithmic design rules. One example is a linked list; it is the …