Abstract
We address a problem of improving the search efficiency of range queries based on Manhattan distance. To this end, we propose a new pivot generation method (the PGM method) formulated as an iterative algorithm, where its convergence is guaranteed within a finite number of iterations. In our experiments using three databases of hand-written characters, newspaper articles and book reviews, we confirmed that our proposed method overcomes a representative conventional method (the BNC method) whose pivots are limited to objects in the datasets, in terms of improvements of objective function values, computation times of pivot selection or generation, the range query performance with arbitrary range setting, and qualitative comparison of visualization results. Moreover, we experimentally show that the PGM method works much better than the BNC method in the case of sparse high-dimensional objects, rather than the case of dense low-dimensional ones.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bustos, B., Navarro, G., Chavez, E.: Pivot Selection Techniques for Proximity Searching in Metric Spaces. Pattern Recognition Lettes 24(14), 2357–2366 (2003)
Chevez, E., Navarro, G.: A compact space decomposition for effective metric indexing. Pattern Recognition Letters 24(9), 1363–1376 (2005)
Jagadish, H.V., Ooi, B.C., Tran, K.L., Yu, C., Zhang, R.: iDistance: An adaptive b+-tree based indexing method for nearest neighbor search. ACM TODS 30(2), 364–397 (2003)
Kurasawa, H., Fukagawa, D., Takasu, A., Adachi, J.: Pivot selection method for optimizing both pruning and balancing in metric space indexes. In: Bringas, P.G., Hameurlain, A., Quirchmayr, G. (eds.) DEXA 2010, Part II. LNCS, vol. 6262, pp. 141–148. Springer, Heidelberg (2010)
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)
Kimura, M., Saito, K., Ueda, N.: Pivot learning for efficient similarity search. In: Apolloni, B., Howlett, R.J., Jain, L. (eds.) KES/WIRN 2007, Part III. LNCS (LNAI), vol. 4694, pp. 227–234. Springer, Heidelberg (2007)
Batko, M., Kohoutkova, P., Novak, D.: CoPhIR Image Collection under the Microscope. In: 2nd International Workshop on Similarity Search and Applications (2009)
Leskovec, J., Krause, A., Guuestrin, C., Faloutsos, C., Van Briesen, J., Glance, N.: Cost-effective outbreak detection in networks. In: Proceeding of the 13th International Conference on Knowledge Discovery and Data Mining, pp. 420–429 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Kobayashi, E., Fushimi, T., Saito, K., Ikeda, T. (2014). Similarity Search by Generating Pivots Based on Manhattan Distance. In: Pham, DN., Park, SB. (eds) PRICAI 2014: Trends in Artificial Intelligence. PRICAI 2014. Lecture Notes in Computer Science(), vol 8862. Springer, Cham. https://doi.org/10.1007/978-3-319-13560-1_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-13560-1_35
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13559-5
Online ISBN: 978-3-319-13560-1
eBook Packages: Computer ScienceComputer Science (R0)