A fully automated method for discovering community structures in high dimensional data

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2009, Pages 968-973

  Ruan, Jianhua ^a  

^a University of Texas at San Antonio   (United States)

Author keywords

Community structure; Image clustering; Modularity

Indexed keywords

ACCURACY INDEX; AFFINITY MATRIX; AUTOMATED METHODS; BIOLOGICAL SCIENCE; COMMUNITY IDENTIFICATION; COMMUNITY STRUCTURES; COMPLEX NETWORKS; HIGH DIMENSIONAL DATA; HIGH DIMENSIONAL SPACES; K-NEAREST NEIGHBORS; NETWORK CONSTRUCTION; NETWORK STRUCTURES; OPTIMAL COMMUNITY; SIMPLE ALGORITHM; SYNTHETIC AND REAL DATA; WEIGHTED GRAPH;

BIOLOGY; CIRCUIT THEORY; CLUSTERING ALGORITHMS; COMPUTER AIDED NETWORK ANALYSIS; SOCIAL SCIENCES;

DATA MINING;

EID: 77951202722     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2009.141     Document Type: Conference Paper

References (19)

1. M. Newman, "The structure and function of complex networks," SIAM Review, vol. 45, pp. 167-256, 2003.
2. M. Newman and M. Girvan, "Finding and evaluating community structure in networks." Phys Rev E, vol. 69, p. 026113, 2004.
3. S. V. Dongen, "Graph clustering via a discrete uncoupling process," SIAM Journal on Matrix Analysis and Applications, vol. 30, pp. 121-141, 2008.
4. B. J. J. Frey and D. Dueck, "Clustering by passing messages between data points." Science, vol. 315, pp. 972-976, 2007.
5. M. Newman, "Modularity and community structure in networks." Proc Natl Acad Sci USA, vol. 103, pp. 8577-82, 2006.
6. L. Danon and et. al., "Comparing community structure identification," J. Stat. Mech., p. P09008, 2005.
7. R. Guimera and L. N. Amaral, "Functional cartography of complex metabolic networks." Nature, vol. 433, pp. 895-900, 2005.
8. J. Ruan and W. Zhang, "Identifying network communities with a high resolution," Physical Review E, vol. 77, no. 1, 2008.
9. S. Fortunato and M. Barthelemy, "Resolution limit in community detection," Proc Natl Acad Sci USA, vol. 104, pp. 36-41, 2007.
10. S. Muff, F. Rao, and A. Caflisch, "Local modularity measure for network clusterizations," Physical Review E, vol. 72, p. 056107, 2005.
11. A. Arenas, A. Fernández, and S. Gómez, "Analysis of the structure of complex networks at different resolution levels," New Journal of Physics, vol. 10, pp. 053 039-+, 2008.
12. A. K. Jain, M. N. Murty, and P. J. Flynn, "Data clustering: A review." ACM Comput. Surv., vol. 31, pp. 264-323, 1999.
13. M. Ester, H. peter Kriegel, J. S, and X. Xu, "A density-based algorithm for discovering clusters in large spatial databases with noise." AAAI Press, 1996, pp. 226-231.
14. G. Karypis, E.-H. S. Han, and V. Kumar, "Chameleon: Hierarchical clustering using dynamic modeling," Computer, vol. 32, pp. 68-75, 1999.
15. V. Chaoji, M. Al Hasan, S. Salem, and M. Zaki, "Sparcl: Efficient and effective shape-based clustering," in Data Mining, 2008. ICDM '08. Eighth IEEE International Conference on, 2008, pp. 93-102.
16. A. Ng, M. Jordan, and Y. Weiss, "On spectral clustering: Analysis and an algorithm." in NIPS, 2001, pp. 849-856.
17. M. Maier, M. Hein, and U. von Luxburg, "Optimal construction of k-nearest-neighbor graphs for identifying noisy clusters," Theor. Comput. Sci., vol. 410, no. 19, pp. 1749- 1764, 2009.
18. R. Guimera, M. Sales-Pardo, and L. N. Amaral, "Modularity from fluctuations in random graphs and complex networks," Physical Review E, vol. 70, p. 025101, 2004.
19. M. Meila, "Comparing clusterings: an axiomatic view," in ICML '05: Proceedings of the 22nd international conference on Machine learning. New York, NY, USA: ACM Press, 2005, pp. 577-584.