Neighborhood graphs for indexing and retrieving multi-dimensional data

Journal of Intelligent Information Systems

Volumn 34, Issue 1, 2010, Pages 93-111

  Hacid, Hakim ^a   Yoshida, Tetsuya ^b  

^a LUCENT TECHNOLOGIES   (United States)

^b HOKKAIDO UNIVERSITY   (Japan)

Author keywords

Graphs; Indexing; Information retrieval; Multi dimensional data; Neighborhood systems; Proximity; Ranking; Similarity measures

Indexed keywords

DELETION METHODS; INDEXING STRUCTURES; MULTI-DIMENSIONAL SPACE; MULTIDIMENSIONAL DATA; NEIGHBORHOOD GRAPHS; NEIGHBORHOOD SEARCH; NEIGHBORHOOD SYSTEMS; POINT LOCATION PROBLEMS; RECURSIVE APPLICATIONS; SIMILARITY MEASURE;

INDEXING (OF INFORMATION); INFORMATION RETRIEVAL; INFORMATION SERVICES;

DATA PROCESSING;

EID: 74849117130     PISSN: 09259902     EISSN: 15737675     Source Type: Journal    
DOI: 10.1007/s10844-009-0081-z     Document Type: Article

References (39)

1. E Anderson 1935 The irises of the gaspé peninsula Bulletin of the American Iris Society 59 2 5
2. Baeza-Yates, R., & Ribeiro-Neto, B. (1999). Modern information retrieval. Reading: Addison Wesley.
3. Beckmann, N., Kriegel, H.-P., Schneider, R., & Seeger, B. (1990). The r-tree: An efficient and robust access method for points and rectangles. In SIGMOD conference (pp. 322-331).
4. C-D Bei RM Gray 1985 An improvement of the minimum distortion encoding algorithm for vector quantization IEEE Transactions on Communications 33 1132 1133 10.1109/TCOM.1985.1096214
5. JL Bentley 1975 Multidimensional binary search trees used for associative searching Communications of the ACM 18 9 509 517 0306.68061 10.1145/361002.361007 904133
6. Berchtold, S., Böhm, C., Keim, D. A., & Kriegel, H.-P. (1997). A cost model for nearest neighbor search in high-dimensional data space. In PODS '97: Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems (pp. 78-86). New York: ACM.
7. C Böhm S Berchtold DA Keim 2001 Searching in high-dimensional spaces: Index structures for improving the performance of multimedia databases ACM Computing Surveys 33 3 322 373 10.1145/502807.502809
8. Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees (pp. 43-49). Belmont, California: Wadsworth International Group.
9. R Fisher 1936 The use of multiple measurements in taxonomic problems Annals of Eugenics 7 179 188
10. JH Friedman F Baskett LJ Shustek 1975 An algorithm for finding nearest neighbors IEEE Transactions on Computers 24 10 1000 1006 0311.68059 10.1109/T-C.1975.224110
11. KR Gabriel RR Sokal 1969 A new statistical approach to geographic variation analysis Systematic Zoology 18 259 278 10.2307/2412323
12. V Gaede O Günther 1998 Multidimensional access methods ACM Computing Surveys 30 2 170 231 10.1145/280277.280279
13. V Gaede O Günther 1998 Multidimensional access methods ACM Computing Surveys 30 2 170 231 10.1145/280277.280279
14. L Guan M Kamel 1992 Equal-average hyperplane partitioning method for vector quantization of image data Pattern Recognition Letters 13 10 693 699 10.1016/0167-8655(92)90098-K (Pubitemid 23374622)
15. Guttman, A. (1984). R-trees: A dynamic index structure for spatial searching. In SIGMOD conference (pp. 47-57).
16. Hacid, H. (2007). Neighborhood graphs for semi-automatic annotation of large image databases. In MMM (1) (pp. 586-595).
17. Hacid, H., & Zighed, D. (2006). Content-based image retrieval in large image databases. In IEEE international conference on granular computing (GrC 2006) (pp. 498-501). Atlanta, USA. (Pubitemid 44761958)
18. Hacid, H., & Zighed, D. A. (2005). An effective method for locally neighborhood graphs updating. In DEXA (pp. 930-939).
19. Henrich, A., Six, H.-W., & Widmayer, P. (1989). The lsd-tree: Spatial access to multidimensional point and nonpoint objects. In Proceedings 15th international conference on very large data bases. Amsterdam.
20. Hettich, S., Blake, C., & Merz, C. (1998). Uci repository of machine learning databases. http://www.ics.uci.edu/ mlearn/MLRepository.html.
21. J Jaromczyk G Toussaint 1992 Relative neighborhood graphs and their relatives P-IEEE 80 1502 1517 10.1109/5.163414 (Pubitemid 23568838)
22. J Katajainen 1988 The region approach for computing relative neighborhood graphs in the lp metric Computing 40 147 161 0628.68055 10.1007/BF02247943 948791
23. Katayama, N., & Satoh, S. (1997). The sr-tree: An index structure for high-dimensional nearest neighbor queries. In SIGMOD conference (pp. 369-380).
24. C-H Lee LH Chen 1994 Fast closest codeword search algorithm for vector quantisation IEE Proceedings Vision, Image, and Signal Process 141 143 148 10.1049/ip-vis:19941140
25. DB Lomet 1992 A review of recent work on multi-attribute access methods SIGMOD Record 21 3 56 63 10.1145/140979.141006
26. Lomet, D. B., & Salzberg, B. (1989). A robust multi-attribute search structure. In ICDE (pp. 296-304).
27. MacQueen, J. B. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of 5-th Berkeley symposium on mathematical statistics and probability (Vol. 1, pp. 281-297). Berkeley: University of California Press.
28. Preparata, F., & Shamos, M. I. (1985). Computationnal geometry-introduction. New York: Springer.
29. Robinson, J. T. (1981). The k-d-b-tree: A search structure for large multidimensional dynamic indexes. In SIGMOD conference (pp. 10-18).
30. H Samet 1984 The quadtree and related hierarchical data structures ACM Computing Surveys 16 2 187 260 10.1145/356924.356930 792572
31. M Scuturici J Clech V-M Scuturici DA Zighed 2005 Topological representation model for image database query Journal of Experimental and Theoretical Artificial Intelligence 17 1-2 145 160 10.1080/0952813051233315945 (Pubitemid 40099954)
32. Smith, W. D. (1989). Studies in computational geometry motivated by mesh generation. PhD thesis, Princeton University.
33. P Somervuo T Kohonen 1999 Self-organizing maps and learning vector quantization for feature sequences Neural Processing Letters 10 2 151 159 10.1023/A:1018741720065
34. GT Toussaint 1980 The relative neighborhood graphs in a finite planar set Pattern Recognition 12 261 268 0437.05050 10.1016/0031-3203(80)90066-7 591314 (Pubitemid 11447300)
35. Toussaint, G. T. (1991). Some insolved problems on proximity graphs. In D. W. Dearholt, & F. Harrary (Eds.), Proc. of the first workshop on proximity graphs. Memoranda in computer and cognitive science MCCS-91-224. Computing research lab. Las Cruces: State University Las Cruces.
36. White, D. A., & Jain, R. (1996). Similarity indexing: Algorithms and performance. In Stor. and retr. for image and video DBs(SPIE) (pp. 62-73).
37. Wojna, A. (2005). Analogy-based reasoning in classifier construction, transactions on. In Rough sets IV, subseries of lecture notes in computer science, LNCS 3700 (pp. 277-374). New York: Springer.
38. YY Yao 2006 Neighborhood systems and approximate retrieval Information Science 176 23 3431 3452 1119.68074 10.1016/j.ins.2006.02.002 (Pubitemid 44310653)
39. Zighed, D., & Hacid, H. (2006). Proximity graphs and separability of classes. In The 11th international conference on information processing and management of uncertainty in knowledge-based systems (IPMU) (pp. 1488-1495). Paris, France.