Skip to main content
SpringerLink
Log in

The Basic Principles of Metric Indexing

  • Chapter
Swarm Intelligence for Multi-objective Problems in Data Mining

Part of the book series: Studies in Computational Intelligence ((SCI,volume 242))

Summary

This chapter describes several methods of similarity search, based on metric indexing, in terms of their common, underlying principles. Several approaches to creating lower bounds using the metric axioms are discussed, such as pivoting and compact partitioning with metric ball regions and generalized hyperplanes. Finally, pointers are given for further exploration of the subject, including non-metric, approximate, and parallel methods.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Santos Filho, R.F., Traina, A., Traina Jr., C., Faloutsos, C.: Similarity search without tears: the OMNI-family of all-purpose access methods. In: Proceedings of the 17th International Conference on Data Engineering, ICDE, pp. 623–630 (2001)

    Google Scholar 

  2. Uribe, R., Navarro, G., Barrientos, R.J., Marín, M.: An Index Data Structure for Searching in Metric Space Databases. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2006. LNCS, vol. 3991, pp. 611–617. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  3. Hjaltason, G.R., Samet, H.: Index-driven similarity search in metric spaces. ACM Transactions on Database Systems, TODS 28(4), 517–580 (2003)

    Article  Google Scholar 

  4. Samet, H.: Foundations of Multidimensional and Metric Data Structures. Morgan Kaufmann, San Francisco (2006)

    MATH  Google Scholar 

  5. : Zhou, X., Wang, G., Yu, J.X., Yu, G.: M + -tree: A new dynamical multidimensional index for metric spaces. In: Zhou, X., Schewe, K.-D. (eds.) Proceedings of the 14th Australasian Database Conference, ADC. Conferences in Research and Practice in Information Technology, vol. 17 (2003)

    Google Scholar 

  6. Zhou, X., Wang, G., Zhou, X., Yu, G.: BM + -tree: A hyperplane-based index method for high-dimensional metric spaces. In: Zhou, L.-z., Ooi, B.-C., Meng, X. (eds.) DASFAA 2005. LNCS, vol. 3453, pp. 398–409. Springer, Heidelberg (2005)

    Google Scholar 

  7. Faloutsos, C., Lin, K.-I.: FastMap: A fast algorithm for indexing, data-mining and visualization of traditional and multimedia. In: Carey, M.J., Schneider, D.A. (eds.) Proceedings of the 1995 ACM SIGMOD International Conference on Management of Data, San Jose, California, pp. 163–174 (1995)

    Google Scholar 

  8. Micó, M.L., Oncina, J.: A new version of the nearest-neighbour approximating and eliminating search algorithm (AESA) with linear preprocessing time and memory requirements. Pattern Recognition Letters 15(1), 9–17 (1994)

    Article  Google Scholar 

  9. Zezula, P., Savino, P., Amato, G., Rabitti, F.: Approximate similarity retrieval with M-Trees. The VLDB Journal 7(4), 275–293 (1998)

    Article  Google Scholar 

  10. Wang, J.T.L., Wang, X., Shasha, D., Zhang, K.: Metricmap: an embedding technique for processing distance-based queries in metric spaces. IEEE Transactions on Systems, Man and Cybernetics, Part B 35(5), 973–987 (2005)

    Article  Google Scholar 

  11. Houle, M.E., Sakuma, J.: Fast approximate similarity search in extremely high-dimensional data sets. In: Proceedings of the 21st IEEE International Conference on Data Engineering, ICDE (2005)

    Google Scholar 

  12. Chávez, E., Figueroa, K., Navarro, G.: Proximity Searching in High Dimensional Spaces with a Proximity Preserving Order. In: Gelbukh, A., de Albornoz, Á., Terashima-Marín, H. (eds.) MICAI 2005. LNCS (LNAI), vol. 3789, pp. 405–414. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  13. Amato, G.: Approximate similarity search in metric spaces. PhD thesis, Computer Science Department – University of Dortmund, August-Schmidt-Str. 12, 44221, Dortmund, Germany (2002)

    Google Scholar 

  14. Ciaccia, P., Patella, M.: PAC nearest neighbor queries: Approximate and controlled search in high-dimensional and metric spaces. In: IEEE Computer Society (eds.) Proceedings of the 16th International Conference on Data Engineering, ICDE, pp. 244–255 (2000)

    Google Scholar 

  15. Amato, G., Rabitti, F., Savino, P., Zezula, P.: Region proximity in metric spaces and its use for approximate similarity search. ACM Transactions on Information Systems, TOIS 21(2), 192–227 (2003)

    Article  Google Scholar 

  16. Karger, D.R., Ruhl, M.: Finding nearest neighbors in growth-restricted metrics. In: Proceedings of the 34th annual ACM symposium on Theory of computing, pp. 741–750. ACM Press, New York (2002)

    Google Scholar 

  17. Skopal, T.: On fast non-metric similarity search by metric access methods. In: Ioannidis, Y., Scholl, M.H., Schmidt, J.W., Matthes, F., Hatzopoulos, M., Böhm, K., Kemper, A., Grust, T., Böhm, C. (eds.) EDBT 2006. LNCS, vol. 3896, pp. 718–736. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  18. Skopal, T.: Unified framework for exact and approximate search in dissimilarity spaces. ACM Transactions on Database Systems, TODS 32(4) (2007)

    Google Scholar 

  19. Dohnal, V., Gennaro, C., Zezula, P.: Similarity join in metric spaces using eD-index. In: Mařík, V., Štěpánková, O., Retschitzegger, W. (eds.) DEXA 2003. LNCS, vol. 2736, pp. 484–493. Springer, Heidelberg (2003)

    Google Scholar 

  20. Bustos, B., Skopal, T.: Dynamic similarity search in multi-metric spaces. In: Proceedings of the 8th ACM international workshop on Multimedia information retrieval. ACM Press, New York (2006)

    Google Scholar 

  21. Fagin, R.: Combining fuzzy nformation from multiple systems. In: Proceedings of the 15th ACM Symposium on Principles of Database Systems, PODS, pp. 216–226. ACM Press, New York (1996)

    Google Scholar 

  22. Fagin, R.: Fuzzy queries in multimedia database systems. In: Proceedings of the 17th ACM Symposium on Principles of Database Systems, PODS, pp. 1–10. ACM Press, New York (1998)

    Google Scholar 

  23. Ciaccia, P., Montesi, D., Penzo, W., Trombetta, A.: Imprecision and user preferences in multimedia queries: A generic algebraic approach. In: Schewe, K.-D., Thalheim, B. (eds.) FoIKS 2000. LNCS, vol. 1762, pp. 50–71. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  24. Ciaccia, P., Patella, M., Zezula, P.: Processing complex similarity queries with distance-based access methods. In: Schek, H.-J., Saltor, F., Ramos, I., Alonso, G. (eds.) EDBT 1998. LNCS, vol. 1377, pp. 9–23. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  25. Ciaccia, P., Patella, M.: Searching in metric spaces with user-defined and approximate distances. ACM Transactions on Database Systems (TODS) 27(4), 398–437 (2002)

    Article  Google Scholar 

  26. Hjaltason, G.R., Samet, H.: Incremental similarity search in multimedia databases. Technical Report CS-TR-4199, Computer Science Department, University of Maryland, College Park (2000)

    Google Scholar 

  27. Batko, M., Gennaro, C., Savino, P., Zezula, P.: Scalable similarity search in metric spaces. In: Proceedings of the DELOS Workshop on Digital Library Architectures, pp. 213–224 (2004)

    Google Scholar 

  28. Falchi, F., Gennaro, C., Zezula, P.: Nearest neighbor search in metric spaces through content-addressable networks. Information Processing & Management 43(3), 665–683 (2007)

    Article  Google Scholar 

  29. Marín, M., Uribe, R., Barrientos, R.J.: Searching and updating metric space databases using the parallel EGNAT. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2007. LNCS, vol. 4487, pp. 229–236. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  30. Alpkocak, A., Danisman, T., Ulker, T.: A parallel similarity search in high dimensional metric space using M-tree. In: Grigoras, D., Nicolau, A., Toursel, B., Folliot, B. (eds.) IWCC 2001. LNCS, vol. 2326, p. 166. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  31. Zezula, P., Savino, P., Rabitti, F., Amato, G., Ciaccia, P.: Processing M-Trees with parallel resources. In: Proceedings of the 8th International Workshop on Research Issues in Data Engineering, RIDE, pp. 147–154. IEEE Computer Society Press, Los Alamitos (1998)

    Google Scholar 

  32. Novak, D., Zezula, P.: M-Chord: a scalable distributed similarity search structure. In: Proceedings of the 1st International Conference on Scalable Information Systems (2006)

    Google Scholar 

  33. 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 

  34. Traina Jr., C., Traina, J.M., Faloutsos, C.: Distance exponent: a new concept for selectivity estimation in metric trees. In: Proceedings of the International Conference on Data Engineering, ICDE (2000)

    Google Scholar 

  35. Belussi, A., Faloutsos, C.: Estimating the selectivity of spatial queries using the correlation fractal dimension. In: Proceedings of the International Conference on Very Large Databases, VLDB (1995)

    Google Scholar 

  36. Traina Jr., C., Traina, A.J.M., Seeger, B., Faloutsos, C.: Slim-Trees: High Performance Metric Trees Minimizing Overlap between Nodes. In: Zaniolo, C., Grust, T., Scholl, M.H., Lockemann, P.C. (eds.) EDBT 2000. LNCS, vol. 1777, pp. 51–65. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  37. Traina Jr., C., Traina, A., Santos Filho, R.F., Faloutsos, C.: How to improve the pruning ability of dynamic metric access methods. In: Proceedings of the eleventh international conference on Information and knowledge management, pp. 219–226 (2002)

    Google Scholar 

  38. Moret, B.M.E.: Towards a discipline of experimental algorithmics. In: Data Structures, Near Neighbor Searches, and Methodology: Fifth and Sixth DIMACS Implementation Challenges, vol. 59, pp. 197–214. Americal Mathematical Society (2002)

    Google Scholar 

  39. Zezula, P., Amato, G., Dohnal, V., Batko, M.: Similarity Search: The Metric Space Approach. Springer, Heidelberg (2006)

    MATH  Google Scholar 

  40. Pestov, V., Stojmirović, A.: Indexing schemes for similarity search: an illustrated paradigm. Fundamenta Informaticae 70(4), 367–385 (2006)

    MATH  MathSciNet  Google Scholar 

  41. Chávez, E., Navarro, G.: Metric databases. In: Rivero, L.C., Doorn, J.H., Ferraggine, V.E. (eds.) Encyclopedia of Database Technologies and Applications, pp. 367–372. Idea Group, USA (2006)

    Google Scholar 

  42. Skopal, T.: Metric Indexing in Information Retrieval. PhD thesis, Technical University of Ostrava (2004)

    Google Scholar 

  43. Deza, E., Deza, M.M.: Dictionary of Distances. Elsevier, Amsterdam (2006)

    Google Scholar 

  44. Kalantari, I., McDonald, G.: A data structure and an algorithm for the nearest point problem. IEEE Transactions on Software Engineering 9(5), 631–634 (1983)

    Article  Google Scholar 

  45. Aronovich, L., Spiegler, I.: Efficient similarity search in metric databases using the CM-tree. Data & Knowledge Engineering (2007)

    Google Scholar 

  46. Brisaboa, N., Pedreira, O., Seco, D., Solar, R., Uribe, R.: Clustering-based similarity search in metric spaces with sparse spatial centers. In: Geffert, V., Karhumäki, J., Bertoni, A., Preneel, B., Návrat, P., Bieliková, M. (eds.) SOFSEM 2008. LNCS, vol. 4910, pp. 186–197. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  47. Beyer, K., Goldstein, J., Ramakrishnan, R., Shaft, U.: When is “nearest neighbor” meaningful? In: Beeri, C., Bruneman, P. (eds.) ICDT 1999. LNCS, vol. 1540, pp. 217–235. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  48. Hetland, M.L.: A survey of recent methods for efficient retrieval of similar time sequences. In: Last, M., Kandel, A., Bunke, H. (eds.) Data Mining in Time Series Databases, vol. 2, pp. 23–42. World Scientific, Singapore (2004)

    Google Scholar 

  49. Zezula, P., Ciaccia, P., Rabitti, F.: M-tree: A dynamic index for similarity queries in multimedia databases. Technical Report 7, HERMES ESPRIT LTR Project (1996)

    Google Scholar 

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

    Google Scholar 

  51. Ciaccia, P., Patella, M., Zezula, P.: A cost model for similarity queries in metric spaces. In: Proceedings of the 17th Symposium on Principles of Database Systems, pp. 59–68 (1998)

    Google Scholar 

  52. Bozkaya, T., Özsoyoglu, M.: Distance-based indexing for high-dimensional metric spaces. In: Proceedings of the, ACM SIGMOD international conference on Management of data, pp. 357–368. ACM Press, New York (1997)

    Chapter  Google Scholar 

  53. Dohnal, V., Gennaro, C., Savino, P., Zezula, P.: Separable splits of metric data sets. In: Proceedings of the Nono Convegno Nazionale Sistemi Evoluti per Basi di Dati, SEBD (June 2001)

    Google Scholar 

  54. Gennaro, C., Savino, P., Zezula, P.: Similarity search in metric databases through hashing. In: Proceedings of the 2001 ACM workshops on Multimedia: multimedia information retrieval (2001)

    Google Scholar 

  55. 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 

  56. Dohnal, V.: An access structure for similarity search in metric spaces. In: Lindner, W., Mesiti, M., Türker, C., Tzitzikas, Y., Vakali, A.I. (eds.) EDBT 2004. LNCS, vol. 3268, pp. 133–143. Springer, Heidelberg (2004)

    Google Scholar 

  57. Dohnal, V.: Indexing Structures for Searching in Metric Spaces. PhD thesis, Masaryk University, Faculty of Informatics (2004)

    Google Scholar 

  58. Semmes, S.: What is a metric space? (September 2007) arXiv:0709.1676v1 [math.MG]

    Google Scholar 

  59. Jain, P.K., Ahmad, K.: Metric Spaces, 2nd edn. Alpha Science International Ltd. Pangbourne (2004)

    Google Scholar 

  60. Arentz, W.A., Hetland, M.L., Olstad, B.: Methods for retrieving musical information based on rhythm and pitch correlations. Journal of New Music Research 34(2) (2005)

    Google Scholar 

  61. Jungnickel, D.: Graphs, Networks and Algorithms, 2nd edn. Springer, Heidelberg (2005)

    MATH  Google Scholar 

  62. Rico-Juan, J.R., Micó, L.: Comparison of AESA and LAESA search algorithms using string and tree-edit-distances. Pattern Recognition Letters 24(9-10), 1417–1426 (2002)

    Article  Google Scholar 

  63. Chávez, E., Marroquín, J.L., Baeza-Yates, R.: Spaghettis: An array based algorithm for similarity queries in metric spaces. In: Proceedings of the String Processing and Information Retrieval Symposium & International Workshop on Groupware (SPIRE), pp. 38–46. IEEE Computer Society Press, Los Alamitos (1999)

    Chapter  Google Scholar 

  64. Bustos, B., Navarro, G., Chávez, E.: Pivot selection techniques for proximity searching in metric spaces. Pattern Recognition Letters 24(14), 2357–2366 (2003)

    Article  MATH  Google Scholar 

  65. Pedreira, O., Brisaboa, N.R.: Spatial Selection of Sparse Pivots for Similarity Search in Metric Spaces. In: van Leeuwen, J., Italiano, G.F., van der Hoek, W., Meinel, C., Sack, H., Plášil, F. (eds.) SOFSEM 2007. LNCS, vol. 4362, pp. 434–445. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  66. Marin, M., Gil-Costa, V., Uribe, R.: Hybrid index for metric space databases. In: Proceedings of the International Conference on Computational Science (2008)

    Google Scholar 

  67. 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 

  68. Vidal, E.: New formulation and improvements of the nearest-neighbour approximating and eliminating search algorithm (AESA). Pattern Recognition Letters 15(1), 1–7 (1994)

    Article  MathSciNet  Google Scholar 

  69. Figueroa, K., Chávez, E., Navarro, G., Paredes, R.: On the least cost for proximity searching in metric spaces. In: Àlvarez, C., Serna, M. (eds.) WEA 2006. LNCS, vol. 4007, pp. 279–290. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  70. Vilar, J.M.: Reducing the overhead of the AESA metric-space nearest neighbour searching algorithm. Information Processing Letters 56(5), 265–271 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  71. Micó, M.L., Oncina, J., Carrasco, R.C.: A fast branch & bound nearest neighbour classifier in metric spaces. Pattern Recognition Letters 17(7), 731–739 (1996)

    Article  Google Scholar 

  72. Tokoro, K., Yamaguchi, K., Masuda, S.: Improvements of TLAESA nearest neighbour search algorithm and extension to approximation search. In: Proceedings of the 29th Australasian Computer Science Conference, pp. 77–83. Australian Computer Society, Inc (2006)

    Google Scholar 

  73. Bozkaya, T., Özsoyoglu, M.: Indexing large metric spaces for similarity search queries. ACM Transactions on Database Systems, TODS 24(3) (1999)

    Google Scholar 

  74. Skopal, T.: Pivoting M-tree: A metric access method for efficient similarity search. In: Snášel, V., Pokorn’y, J., Richta, K. (eds.) Proceedings of the Annual International Workshop on DAtabases, TExts, Specifications and Objects (DATESO), Desna, Czech Republic, Technical University of Aachen (RWTH), April 2004. CEUR Workshop Proceedings, vol. 98 (2004)

    Google Scholar 

  75. Uhlmann, J.K.: Metric trees. Applied Mathematics Letters 4(5), 61–62 (1991)

    Article  MATH  MathSciNet  Google Scholar 

  76. Yianilos, P.N.: Data structures and algorithms for nearest neighbor search in general metric spaces. In: Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms, Philadelphia, PA, USA, pp. 311–321. Society for Industrial and Applied Mathematics

    Google Scholar 

  77. Chiueh, T.-c.: Content-based image indexing. In: Proceedings of the Twentieth International Conference on Very Large Databases, VLDB, Santiago, Chile, pp. 582–593 (1994)

    Google Scholar 

  78. Yianilos, P.N.: Excluded middle vantage point forests for nearest neighbor search. Technical report, NEC Research Institute (1999)

    Google Scholar 

  79. chee Fu, A.W., shuen Chan, P.M., Cheung, Y.-L., Moon, Y.S.: Dynamic vp-tree indexing for n-nearest neighbor search given pair-wise distances. The VLDB Journal 9(2), 154–173 (2000)

    Article  Google Scholar 

  80. Chávez, E., Navarro, G.: An effective clustering algorithm to index high dimensional metric spaces. In: Proceedings of the 6th International Symposium on String Processing and Information Retrieval SPIRE, pp. 75–86. IEEE Computer Society Press, Los Alamitos (2000)

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  82. Fredriksson, K.: Exploiting distance coherence to speed up range queries in metric indexes. Information Processing Letters 95(1), 287–292 (2005)

    Article  MathSciNet  Google Scholar 

  83. Fredriksson, K.: Engineering efficient metric indexes. Pattern Recognition Letters (2006)

    Google Scholar 

  84. Vieira, M.R., Traina Jr., C., Takada Chino, F.J., Traina, A.J.M.: DBM-tree: a dynamic metric access method sensitive to local density data. In: Proceedings of the 19th Brazilian Symposium on Databases (SBBD). UnB (2004)

    Google Scholar 

  85. Vieira, M.R., Traina Jr., C., Takada Chino, F.J., Machado Traina, A.J.: DBM-tree: trading height-balancing for performance in metric access methods. Journal of the Brazilian Computer Society 11(3), 20–39 (2006)

    Google Scholar 

  86. Frank, K.H.A.: Dehne and Hartmut Noltemeier. Voronoi trees and clustering problems. Information Systems 12(2), 171–175 (1987)

    Google Scholar 

  87. Noltemeier, H.: Voronoi trees and applications. In: Proceedings of the International Workshop on Discrete Algorithms and Complexity, pp. 69–74 (1989)

    Google Scholar 

  88. Noltemeier, H., Verbarg, K., Zirkelbach, C.: Monotonous Bisector* trees: a tool for efficient partitioning of complex scenes of geometric objects. In: Monien, B., Ottmann, T. (eds.) Data Structures and Efficient Algorithms. LNCS, vol. 594, pp. 186–203. Springer, Heidelberg (1992)

    Google Scholar 

  89. Noltemeier, H., Verbarg, K., Zirkelbach, C.: A data structure for representing and efficient querying large scenes of geometric objects: MB* trees. In: Farin, G.E., Hagen, H., Noltemeier, H., Knödel, W. (eds.) Geometric Modelling. Computing Supplement, vol. 8. Springer, Heidelberg (1992)

    Google Scholar 

  90. Liu, B., Wang, Z., Yang, X., Wang, W., Shi, B.-L.: A Bottom-Up Distance-Based Index Tree for Metric Space. In: Wang, G.-Y., Peters, J.F., Skowron, A., Yao, Y. (eds.) RSKT 2006. LNCS (LNAI), vol. 4062, pp. 442–449. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  91. Ciaccia, P., Patella, M., Rabitti, F., Zezula, P.: Indexing metric spaces with M-tree. In: Cristiani, M., Tanca, L. (eds.) Atti del Quinto Convegno Nazionale su Sistemi Evoluti per Basi di Dati (SEBD 1997), Verona, Italy, June 1997, pp. 67–86 (1997)

    Google Scholar 

  92. Skopal, T., Pokorný, J., Krátký, M., Snášel, V.: Revisiting M-tree building principles. In: Kalinichenko, L.A., Manthey, R., Thalheim, B., Wloka, U. (eds.) ADBIS 2003. LNCS, vol. 2798, pp. 148–162. Springer, Heidelberg (2003)

    Google Scholar 

  93. Cantone, D., Ferro, A., Pulvirenti, A., Recupero, D.R., Shasha, D.: Antipole tree indexing to support range search and k-nearest neighbor search in metric spaces. IEEE Transactions on Knowledge and Data Engineering 17(4), 535–550 (2005)

    Article  Google Scholar 

  94. Skopal, T., Hoksza, D.: Improving the performance of M-tree family by nearest-neighbor graphs. In: Ioannidis, Y., Novikov, B., Rachev, B. (eds.) ADBIS 2007. LNCS, vol. 4690, pp. 172–188. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  95. Manolopoulos, Y., Nanopoulos, A., Papadopoulos, A.N., Theodoridis, Y.: R-Trees: Theory and Applications. In: Advanced Information and Knowledge Processing. Springer, Heidelberg (2006)

    Google Scholar 

  96. Yu, C., Ooi, B.C., Tan, K.-L., Jagadish, H.V.: Indexing the distance: An efficient method to KNN processing. In: Proceedings of the 27th International Conference on Very Large Data Bases, VLDB, San Francisco, CA, USA, pp. 421–430. Morgan Kaufmann Publishers Inc, San Francisco (2001)

    Google Scholar 

  97. Ishikawa, M., Chen, H., Furuse, K., Yu, J.X., Ohbo, N.: MB+Tree: A Dynamically Updatable Metric Index for Similarity Search. In: Lu, H., Zhou, A. (eds.) WAIM 2000. LNCS, vol. 1846, pp. 356–373. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  98. Uhlmann, J.K.: Satisfying general proximity/similarity queries with metric trees. Information Processing Letters 40(4), 175–179 (1991)

    Article  MATH  Google Scholar 

  99. Brin, S.: Near neighbor search in large metric spaces. In: Dayal, U., Gray, P.M.D., Nishio, S. (eds.) Proceedings of 21th International Conference on Very Large Data Bases, VLDB, pp. 574–584. Morgan Kaufman, San Francisco (1995)

    Google Scholar 

  100. Navarro, G.: Searching in metric spaces by spatial approximation. In: Proceedings of the 6th International Symposium on String Processing and Information Retrieval, SPIRE, pp. 141–148. IEEE Computer Society Press, Los Alamitos (1999)

    Google Scholar 

  101. Navarro, G.: Searching in metric spaces by spatial approximation. The VLDB Journal 11(1), 28–46 (2002)

    Article  Google Scholar 

  102. Navarro, G., Reyes, N.: Dynamic spatial approximation trees. In: Proceedings of the XXI Conference of the Chilean Computer Science Society, SCCC, pp. 213–222 (2001)

    Google Scholar 

  103. 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 

  104. Navarro, G., Reyes, N.: Improved deletions in dynamic spatial approximation trees. In: Proceedings of the XXIII International Conference of the Chilean Computer Science Society, SCCC (2003)

    Google Scholar 

  105. Arroyuelo, D., Navarro, G., Reyes, N.: Fully dynamic and memory-adaptative spatial approximation trees. In: Proceedings of the Argentinian Congress in Computer Science, CACIC, pp. 1503–1513 (2003)

    Google Scholar 

  106. Arroyuelo, D., Muñoz, F., Navarro, G., Reyes, N.: Memory-adaptative dynamic spatial approximation trees. In: Proceedings of the 10th International Symposium on String Processing and Information Retrieval, SPIRE (2003)

    Google Scholar 

  107. Chavez, E., Herrera, N., Reyes, N.: Spatial approximation + sequential scan = efficient metric indexing. In: Proceedings of the XXIV International Conference of the Chilean Computer Science Society, SCCC (2004)

    Google Scholar 

  108. Schroeder, M.: Fractals, Chaos, Power Laws, 6th edn. W. H. Freeman and Company, New York (1991)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer and Information Science, Norwegian University of Science and Technology, Sem Sælands vei 7-9, NO-7491, Trondheim, Norway

    Magnus Lie Hetland

Authors
  1. Magnus Lie Hetland
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Departamento de Computacion, CINVESTAV-IPN, Av. IPN No. 2508, Col. San Pedro Zacatenco, Mexico, D.F., 07360

    Carlos Artemio Coello Coello

  2. Department of information and Communication Technology, FakirMohan University, VyasaVihar, 756 019, Balasore, Orissa, India

    Satchidananda Dehuri

  3. Department of Computer Science and Engineering, Jadavpur University, Raja S.C.Mallik Road, 700 032, Kolkata, India

    Susmita Ghosh

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Hetland, M.L. (2009). The Basic Principles of Metric Indexing. In: Coello, C.A.C., Dehuri, S., Ghosh, S. (eds) Swarm Intelligence for Multi-objective Problems in Data Mining. Studies in Computational Intelligence, vol 242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03625-5_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-03625-5_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03624-8

  • Online ISBN: 978-3-642-03625-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation