Abstract
In this paper, we propose an effective indexing and search algorithms for approximate K-NN based on an enhanced implementation of the Metric Suffix Array and Permutation-Based Indexing. Our main contribution is to propose a sound scalable strategy to prune objects based on the location of the reference objects in the query ordered lists. We study the performance and efficiency of our algorithms on large-scale dataset of millions of documents. Experimental results show a decrease of computational time while preserving the quality of the results.
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Mohamed, H., Marchand-Maillet, S. (2013). Permutation-Based Pruning for Approximate K-NN Search. In: Decker, H., Lhotská, L., Link, S., Basl, J., Tjoa, A.M. (eds) Database and Expert Systems Applications. DEXA 2013. Lecture Notes in Computer Science, vol 8055. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40285-2_6
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DOI: https://doi.org/10.1007/978-3-642-40285-2_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40284-5
Online ISBN: 978-3-642-40285-2
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