Can shared-neighbor distances defeat the curse of dimensionality?

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

Volumn 6187 LNCS, Issue , 2010, Pages 482-500

  Houle, Michael E ^a   Kriegel, Hans Peter ^b   Kröger, Peer ^b   Schubert, Erich ^b   Zimek, Arthur ^b  

^a NATIONAL INSTITUTE OF INFORMATICS   (Japan)

^b UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CURSE OF DIMENSIONALITY; DATA DISTRIBUTION; DATA MINING APPLICATIONS; DATA OBJECTS; DATA SETS; DISTANCE MEASURE; DISTRIBUTED DATA; SIMILARITY MEASURE;

MANAGEMENT INFORMATION SYSTEMS;

EID: 77955045250     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-13818-8_34     Document Type: Conference Paper

References (37)

1. 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)
2. Hinneburg, A., Aggarwal, C.C., Keim, D.A.: What is the nearest neighbor in high dimensional spaces? In: Proc. VLDB (2000)
3. Aggarwal, C.C., Hinneburg, A., Keim, D.: On the surprising behavior of distance metrics in high dimensional space. In: Van den Bussche, J., Vianu, V. (eds.) ICDT 2001. LNCS, vol.1973, p. 420. Springer, Heidelberg (2000)
4. Bennett, K.P., Fayyad, U., Geiger, D.: Density-based indexing for approximate nearest-neighbor queries. In: Proc. KDD (1999)
5. Kriegel, H.P., Kröger, P., Zimek, A.: Clustering high dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering. ACM TKDD 3(1), 1-58 (2009)
6. Aggarwal, C.C.: Re-designing distance functions and distance-based applications for high dimensional data. SIGMOD Record 30(1), 13-18 (2001)
7. Domeniconi, C., Papadopoulos, D., Gunopulos, D., Ma, S.: Subspace clustering of high dimensional data. In: Proc. SDM (2004)
8. Woo, K.G., Lee, J.H., Kim, M.H., Lee, Y.J.: FINDIT: a fast and intelligent subspace clustering algorithm using dimension voting. Inform. Software Technol. 46(4), 255-271 (2004)
9. Parsons, L., Haque, E., Liu, H.: Subspace clustering for high dimensional data: A review. SIGKDD Explorations 6(1), 90-105 (2004)
10. Yiu, M.L., Mamoulis, N.: Iterative projected clustering by subspace mining. IEEE TKDE 17(2), 176-189 (2005)
11. Liu, G., Li, J., Sim, K., Wong, L.: Distance based subspace clustering with flexible dimension partitioning. In: Proc. ICDE (2007)
12. Kriegel, H.P., Kröger, P., Schubert, E., Zimek, A.: A general framework for increasing the robustness of PCA-based correlation clustering algorithms. In: Ludäscher, B., Mamoulis, N. (eds.) SSDBM 2008. LNCS, vol.5069, pp. 418-435. Springer, Heidelberg (2008)
13. Moise, G., Sander, J.: Finding non-redundant, statistically significant regions in high dimensional data: a novel approach to projected and subspace clustering. In: Proc. KDD (2008)
14. Achtert, E., Böhm, C., David, J., Kröger, P., Zimek, A.: Global correlation clustering based on the Hough transform. Stat. Anal. Data Min. 1(3), 111-127 (2008)
15. Aggarwal, C.C., Yu, P.S.: Outlier detection for high dimensional data. In: Proc. SIGMOD (2001)
16. Zhu, C., Kitagawa, H., Faloutsos, C.: Example-based robust outlier detection in high dimensional datasets. In: Proc. ICDM (2005)
17. Kriegel, H.P., Schubert, M., Zimek, A.: Angle-based outlier detection in highdimensional data. In: Proc. KDD (2008)
18. Müller, E., Assent, I., Steinhausen, U., Seidl, T.: OutRank: ranking outliers in high dimensional data. In: Proc. ICDE Workshop DBRank (2008)
19. Katayama, N., Satoh, S.: Distinctiveness-sensitive nearest-neighbor search for efficient similarity retrieval of multimedia information. In: Proc. ICDE (2001)
20. Aggarwal, C.C., Yu, P.S.: Finding generalized projected clusters in high dimensional space. In: Proc. SIGMOD (2000)
21. Berchtold, S., Böhm, C., Jagadish, H.V., Kriegel, H.P., Sander, J.: Independent Quantization: An index compression technique for high-dimensional data spaces. In: Proc. ICDE (2000)
22. Jin, H., Ooi, B.C., Shen, H.T., Yu, C., Zhou, A.Y.: An adaptive and efficient dimensionality reduction algorithm for high-dimensional indexing. In: Proc. ICDE (2003)
23. Aggarwal, C.C., Yu, P.S.: On high dimensional indexing of uncertain data. In: Proc. ICDE (2008)
24. Francois, D., Wertz, V., Verleysen, M.: The concentration of fractional distances. IEEE TKDE 19(7), 873-886 (2007)
25. Ertöz, L., Steinbach, M., Kumar, V.: Finding clusters of different sizes, shapes, and densities in noisy, high dimensional data. In: Proc. SDM (2003)
26. Houle, M.E.: Navigating massive data sets via local clustering. In: Proc. KDD (2003)
27. Guha, S., Rastogi, R., Shim, K.: CURE: An efficient clustering algorithm for large databases. In: Proc. SIGMOD, pp. 73-84 (1998)
28. Jarvis, R.A., Patrick, E.A.: Clustering using a similarity measure based on shared near neighbors. IEEE TC C-22(11), 1025-1034 (1973)
29. Houle, M.E.: The relevant-set correlation model for data clustering. Stat. Anal. Data Min. 1(3), 157-176 (2008)
30. Kriegel, H.P., Kröger, P., Schubert, E., Zimek, A.: Outlier detection in axis-parallel subspaces of high dimensional data. In: Proc. PAKDD (2009)
31. Faloutsos, C., Kamel, I.: Beyond uniformity and independence: Analysis of R-trees using the concept of fractal dimension. In: Proc. SIGMOD (1994)
32. Belussi, A., Faloutsos, C.: Estimating the selectivity of spatial queries using the 'correlation' fractal dimension. In: Proc. VLDB (1995)
33. Pagel, B.U., Korn, F., Faloutsos, C.: Deflating the dimensionality curse using multiple fractal dimensions. In: Proc. ICDE (2000)
34. Korn, F., Pagel, B.U., Falutsos, C.: On the "dimensionality curse" and the "selfsimilarity blessing". IEEE TKDE 13(1), 96-111 (2001)
35. Asuncion, A., Newman, D.J.: UCI Machine Learning Repository (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
36. Geusebroek, J.M., Burghouts, G.J., Smeulders, A.: The Amsterdam Library of Object Images. Int. J. Computer Vision 61(1), 103-112 (2005)
37. Boujemaa, N., Fauqueur, J., Ferecatu, M., Fleuret, F., Gouet, V., Saux, B.L., Sahbi, H.: IKONA: Interactive generic and specific image retrieval. In: Proc. MMCBIR, pp. 25-28 (2001)