A set correlation model for partitional clustering

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

Volumn 6118 LNAI, Issue PART 1, 2010, Pages 4-15

  Vinh, Nguyen Xuan ^a,b,c   Houle, Michael E ^c  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

^b CSIRO   (Australia)

^c NATIONAL INSTITUTE OF INFORMATICS   (Japan)

Author keywords

Clustering; Correlation; RSC; SASH; Shared neighbor

Indexed keywords

CLUSTERING; CORRELATION; RSC; SASH; SHARED NEIGHBOR;

CLUSTERING ALGORITHMS; DATA MINING;

CLUSTER ANALYSIS;

EID: 79956307129     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-13657-3_4     Document Type: Conference Paper

References (19)

1. Jarvis, R.A., Patrick, E.A.: Clustering using a similarity measure based on shared near neighbors. IEEE Trans. Comput. 22(11), 1025-1034 (1973)
2. Guha, S., Rastogi, R., Shim, K.: Rock: A robust clustering algorithm for categorical attributes. Information Systems, 512-521 (1999)
3. Ertöz, L., Steinbach, M., Kumar, V.: Finding clusters of different sizes, shapes, and densities in noisy, high dimensional data. In: Proc. 3rd SIAM Intern. Conf. on Data Mining, SDM (2003)
4. Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proc. 2nd Int. Conf. on Knowl. Discovery and Data Mining (KDD), pp. 226-231. AAAI Press, Menlo Park (1996)
5. Houle, M.E.: The relevant-set correlation model for data clustering. Stat. Anal. Data Min. 1(3), 157-176 (2008)
6. Dasgupta, S.: The hardness of k-means clustering. Technical Report CS2007-0890, University of California, San Diego (2008)
7. Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 2nd edn. MIT Press, McGraw-Hill Book Company (2000)
8. Houle, M.E., Sakuma, J.: Fast approximate similarity search in extremely highdimensional data sets. In: ICDE 2005: Proceedings of the 21st International Conference on Data Engineering, Washington, DC, USA, pp. 619-630. IEEE Computer Society, Los Alamitos (2005) (Pubitemid 41731188)
9. Elkan, C.: Using the triangle inequality to accelerate k-means. In: Proceedings of the Twentieth International Conference on Machine Learning, ICML 2003 (2003)
10. Karypis, G.: CLUTO - a clustering toolkit (2002)
11. Lawrence, H., Phipps, A.: Comparing partitions. Journal of Classification 2(1), 193-218 (1985)
12. Vinh, N.X., Epps, J., Bailey, J.: Information theoretic measures for clusterings comparison: Is a correction for chance necessary? In: ICML 2009: Proceedings of the 26th international conference on Machine learning (2009)
13. Yeung, K.Y.: Cluster analysis of gene expression data. PhD thesis, University of Washington, Seattle, WA (2001)
14. Yeung, K., Medvedovic, M., Bumgarner, R.: Clustering gene-expression data with repeated measurements. Genome Biology 4(5), R34 (2003)
15. Geusebroek, J.M., Burghouts, G.J., Smeulders, A.W.M.: The amsterdam library of object images. Int. J. Comput. Vision 61(1), 103-112 (2005)
16. Boujemaa, N., Fauqueur, J., Ferecatu, M., Fleuret, F., Gouet, V., LeSaux, B., Sahbi, H.: Ikona: Interactive specific and generic image retrieval. In: International workshop on Multimedia ContentBased Indexing and Retrieval, MMCBIR 2001 (2001)
17. Lewis, D.D., Yang, Y., Rose, T.G., Li, F.: RCV1: A new benchmark collection for text categorization research. J. Mach. Learn. Res. 5, 361-397 (2004)
18. Asuncion, A., Newman, D.: UCI machine learning repository (2007)
19. Dasgupta, S.: Experiments with random projection. In: UAI 2000: Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence, pp. 143-151. Morgan Kaufmann Publishers Inc., San Francisco (2000)