Luis González Ares

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We consider the problem of similarity search in metric spaces with costly distance functions and large databases. There is a trade-off between the amount of information stored in the index and the reduction in the number of comparisons for solving a query. Pivot-based methods clearly outperform clustering-based ones in number of comparisons, but their space(More)
Clustering-based methods for searching in metric spaces partition the space into a set of disjoint clusters. When solving a query, some clusters are discarded without comparing them with the query object, and clusters that can not be discarded are searched exhaustively. In this paper we propose a new strategy and algorithms for clustering-based methods that(More)
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