Abstract
In this paper we present our new LVA-Index for indexing multidimensional data. The LVA-Index has a layered structure improving performance when searching for nearest neighbors. The index combines some features of the VAFile and the NBC algorithm, namely: the idea of approximation of the data vectors and the idea of layers. The crucial advantage of the LVA-Index is that it stores n neighbor layers for each cell. For this reason, contrary to the VA-File, the LVA-Index does not require scanning of the entire approximation file. Our experiments proved that searching using the LVA-Index is faster than searching using the VA-File which was designed to effectively handle multidimensional data.
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Lasek, P. (2009). LVA-Index: An Efficient Way to Determine Nearest Neighbors. In: Cyran, K.A., Kozielski, S., Peters, J.F., Stańczyk, U., Wakulicz-Deja, A. (eds) Man-Machine Interactions. Advances in Intelligent and Soft Computing, vol 59. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00563-3_65
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DOI: https://doi.org/10.1007/978-3-642-00563-3_65
Publisher Name: Springer, Berlin, Heidelberg
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