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Recursive Lists of Clusters: A Dynamic Data Structure for Range Queries in Metric Spaces

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Computer and Information Sciences - ISCIS 2005 (ISCIS 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3733))

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Abstract

We introduce a novel data structure for solving the range query problem in generic metric spaces. It can be seen as a dynamic version of the List of Clusters data structure of Chávez and Navarro. Experimental results show that, with respect to range queries, it outperforms the original data structure when the database dimension is below 12. Moreover, the building process is much more efficient, for any size and any dimension of the database.

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References

  1. Arroyuelo, D., Muñoz, F., Navarro, G., Reyes, N.: Memory-adaptative dynamic spatial approximation trees. In: Nascimento, M.A., de Moura, E.S., Oliveira, A.L. (eds.) SPIRE 2003. LNCS, vol. 2857, pp. 360–368. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  2. Baeza-Yates, R., Cunto, W., Manber, U., Wu, S.: Proximity matching using fixed-queries trees. In: Crochemore, M., Gusfield, D. (eds.) CPM 1994. LNCS, vol. 807, pp. 198–212. Springer, Heidelberg (1994)

    Google Scholar 

  3. Brin, S.: Near neighbor search in large metric spaces. In: Proc. of the 21st Int. Conf. on Very Large Data Bases, pp. 574–584. Morgan Kaufmann, San Francisco (1995)

    Google Scholar 

  4. Burkhard, W.A., Keller, R.M.: Some approaches to best-match file searching. Communications of the ACM 16(4), 230–236 (1973)

    Article  MATH  Google Scholar 

  5. Chávez, E., Navarro, G.: An effective clustering algorithm to index high dimensional metric spaces. In: Proc. of the 7th Symp. on String Processing and Information Retrieval, pp. 75–86. IEEE CS Press, Los Alamitos (2000)

    Chapter  Google Scholar 

  6. Chávez, E., Navarro, G.: A compact space decomposition for effective metric indexing. Pattern Recognition Letters 26(9), 1363–1376 (2005)

    Article  Google Scholar 

  7. Chávez, E., Navarro, G., Baeza-Yates, R., Marroquín, J.L.: Searching in metric spaces. ACM Computing Surveys 33(3), 273–321 (2001)

    Article  Google Scholar 

  8. Ciaccia, P., Patella, M., Zezula, P.: M-tree: An efficient access method for similarity search in metric spaces. In: Proc. of the 23rd Int. Conf. on Very Large Data Bases, pp. 426–435. Morgan Kaufmann, San Francisco (1997)

    Google Scholar 

  9. Dohnal, V., Gennaro, C., Savino, P., Zezula, P.: D-index: Distance searching index for metric data sets. Multimedia Tools and Applications 21(1), 9–33 (2003)

    Article  Google Scholar 

  10. Navarro, G., Reyes, N.: Fully dynamic spatial approximation trees. In: Laender, A.H.F., Oliveira, A.L. (eds.) SPIRE 2002. LNCS, vol. 2476, pp. 254–270. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  11. Ruiz, E.V.: An algorithm for finding nearest neighbours in (approximately) constant average time. Pattern Recognition Letters 4(3), 145–157 (1986)

    Article  Google Scholar 

  12. Traina Jr., C., Traina, A., Filho, R.S., Faloutsos, C.: How to improve the pruning ability of dynamic metric access methods. In: Proc. of the 11th Int. Conf. on Information and Knowledge Management, pp. 219–226. ACM Press, New York (2002)

    Google Scholar 

  13. Yianilos, P.N.: Data structures and algorithms for nearest neighbor search in general metric spaces. In: Proc. of the 4th Annual ACM-SIAM Symp. on Discrete Algorithms, pp. 311–321 (1993)

    Google Scholar 

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Mamede, M. (2005). Recursive Lists of Clusters: A Dynamic Data Structure for Range Queries in Metric Spaces. In: Yolum, p., Güngör, T., Gürgen, F., Özturan, C. (eds) Computer and Information Sciences - ISCIS 2005. ISCIS 2005. Lecture Notes in Computer Science, vol 3733. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11569596_86

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  • DOI: https://doi.org/10.1007/11569596_86

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29414-6

  • Online ISBN: 978-3-540-32085-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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