Extending fast nearest neighbour search algorithms for approximate k-NN classification

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2652, Issue , 2003, Pages 589-597

  Moreno Seco, Francisco ^a   Micó, Luisa ^a   Oncina, Jose ^a  

^a UNIVERSITY OF ALICANTE   (Spain)

Author keywords

Classification Rule; Nearest Neighbour; Pattern Recognition

Indexed keywords

ALGORITHMS; CLUSTERING ALGORITHMS; DATA MINING; LEARNING ALGORITHMS; PATTERN RECOGNITION; TREES (MATHEMATICS);

CLASSIFICATION RESULTS; CLASSIFICATION RULES; COMPUTATIONAL OVERHEADS; HIGH DIMENSIONAL DATA; K NEAREST NEIGHBOURS (K-NN); NEAREST NEIGHBOUR; NEAREST NEIGHBOUR SEARCH; NEAREST-NEIGHBOUR SEARCH ALGORITHMS;

NEAREST NEIGHBOR SEARCH;

EID: 33749634755     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-44871-6_69     Document Type: Article

References (19)

1. Alinat, P.: Periodic progress report 4, ROARS project ESPRIT II - Number 5516. Thomson Technical Report TS ASM 93/S/EGS/NC/079 (1993)
2. st VLDB Conference (1995) 574-584
3. Duda, R., Hart, P.: Pattern Classification and Scene Analysis. Wiley (1973)
4. Friedman, J. H., Bentley, J. L., Finkel, R. A.: An algorithm for finding best matches in logarithmic expected time. ACM Transactions on Mathematical Software (1977) 3 209-226
5. Fukunaga, K., Narendra, M.: A branch and bound algorithm for computing k-nearest neighbors. IEEE Trans. Computing (1975) 24 750-753
6. Granum, E., Thomason, M. G.: Automatically inferred Markov network models for classification of chromosomal band pattern structures. Cytometry (1990) 11 26-39
7. Granum, E., Thomason, M. G., Gregor, J.: On the use of automatically inferred Markov networks for chromosome analysis. In Automation of Cytogenetics, C. Lundsteen and J. Piper, eds., Springer-Verlag (1989) 233-251
8. Jain, A. K., Dubes, R. C.: Algorithms for clustering data. Prentice-Hall (1988)
9. Lundsteen, C., Phillip, J., Granum, E.: Quantitative analysis of 6985 digitized trypsin G-banded human metaphase chromosomes. Clinical Genetics (1980) 18 355-370
10. Moreno-Seco, F., Micó, L., Oncina, J.: Extending LAESA fast nearest neighbour algorithm to find the k nearest neighbours. Structural, Syntactic, and Statistical Pattern Recognition. Lecture Notes in Computer Science, T. Caelly et al (Eds.) vol. 2396, Springer-Verlag (2002) 691-699
11. Moreno-Seco, F., Micó, L., Oncina, J.: A modification of the LAESA algorithm for approximated k-NN classification. Pattern Recognition Letters (2003) 24 (1-3) 47-53
12. Micó, L., Oncina, J., Carrasco, R. C.: A fast branch and bound nearest neighbour classifier in metric spaces. Pattern Recognition Letters (1996) 17 731-739
13. Micó, L., Oncina, J., Vidal, E.: A new version of the nearest neighbour approximating and eliminating search algorithm (AESA) with linear preprocessing-time and memory requirements. Pattern Recognition Letters (1994) 15 9-17
14. Nene, S., Nayar, S.: A Simple Algorithm for Nearest Neighbor Search in High Dimensions. IEEE Transactions on Pattern Analysis and Machine Intelligence (1997) 19(9) 989-1003
15. Ramasubramanian, R., Paliwal, K. K.: Fast nearest-neighbor search algorithms based on approximation-elimination search. Pattern Recognition 33 (2000) 1497-1510
16. Vidal, E.: New formulation and improvements of the Nearest-Neighbour Approximating and Eliminating Search Algorithm (AESA). Pattern Recognition Letters (1994) 15 1-7
17. Wagner, R. A., Fischer, M. J.: The String-to-String Correction Problem. Journal of the Association for Computing Machinery (1974) 21(1) 168-173
18. Yianilos, P. N.: Data Structures and Algorithms for Nearest Neighbor Search in General Metric Spaces. ACM-SIAM Symposium on Discrete Algorithms (1993) 311-321
19. Zhang, K., Shasha, D.: Simple fast algorithms for the editing distance between trees and related problems. SIAM Journal of Computing (1989) 18 1245-1262