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LVA-Index: An Efficient Way to Determine Nearest Neighbors

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Man-Machine Interactions

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 59))

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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References

  1. Blott, S., Weber, R.: A simple vector approximation file for similarity search in high-dimensional vector spaces. Technical Report 19, ESPRIT project HERMES (no. 9141) (1997)

    Google Scholar 

  2. Finkel, R.A., Bentley, J.L.: Quad-trees: A data structure for retrieval on composite keys. ACTA Informatica 4(1), 1–9 (1974)

    Article  MATH  Google Scholar 

  3. Guttman, A.: R-trees: A dynamic index structure for spatial searching. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, Boston, US, pp. 47–57 (1984)

    Google Scholar 

  4. Nievergelt, J., Hinterberger, H., Sevcik, K.C.: The grid file: An adaptable symmetric multikey file structure. ACM Transactions on Database Systems 9(1), 38–71 (1984)

    Article  Google Scholar 

  5. Orenstein, J.A., Merrett, T.H.: A class of data structures for associative searching. In: Proceedings of the ACM Symposium on Principles of Database Systems, Waterloo, Canada, pp. 181–190 (1984)

    Google Scholar 

  6. Robinson, J.T.: The k-d-b-tree: A search structure for large multidimensional dynamic indexes. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 10–18 (1981)

    Google Scholar 

  7. Weber, R., Schek, H.J., Blott, S.: A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces. In: Proceedings of the 24th VLDB Conference on Very Large Data Bases, New York, US, pp. 194–205 (1998)

    Google Scholar 

  8. Zhou, S., Zhao, Y., Guan, J., Huang, J.: A neighborhood-based clustering algorithm. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS, vol. 3518, pp. 361–371. Springer, Heidelberg (2005)

    Google Scholar 

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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

  • Print ISBN: 978-3-642-00562-6

  • Online ISBN: 978-3-642-00563-3

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