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The problem of searching the elements of a set that are close to a given query element under some similarity criterion has a vast number of applications in many branches of computer science, from pattern recognition to textual and multimedia information retrieval. We are interested in the rather general case where the similarity criterion defines a metric(More)
The metric space model abstracts many proximity search problems, from nearest-neighbor classifiers to textual and multimedia information retrieval. In this context, an index is a data structure that speeds up proximity queries. However , indexes lose their efficiency as the intrinsic data dimensionality increases. In this paper we present a simple index(More)
We introduce a new probabilistic proximity search algorithm for range and K-nearest neighbor (K-NN) searching in both coordinate and metric spaces. Although there exist solutions for these problems, they boil down to a linear scan when the space is intrinsically high-dimensional, as is the case in many pattern recognition tasks. This, for example, renders(More)
A model to query document databases by both their content and structure is presented. The goal is to obtain a query language that is expressive in practice while being efficiently implementable, features not present at the same time in previous work. The key ideas of the model are a set-oriented query language based on operations on nearby structure(More)