Distributed approximate spectral clustering for large-scale datasets

HPDC '12 - Proceedings of the 21st ACM Symposium on High-Performance Parallel and Distributed Computing

Volumn , Issue , 2012, Pages 223-234

  Gao, Fei ^a   Abd Almageed, Wael ^b   Hefeeda, Mohamed ^c  

^a SIMON FRASER UNIVERSITY   (Canada)

^b UNIVERSITY OF MARYLAND   (United States)

^c QATAR COMPUTING RESEARCH INSTITUTE   (Qatar)

Author keywords

Distributed clustering; Kernel based algorithms; Large data sets; Spectral clustering

Indexed keywords

COMPUTING RESOURCE; DATA-INTENSIVE APPLICATION; DISTRIBUTED CLUSTERING; HIGH RATE; KERNEL MATRICES; LARGE DATASETS; LARGE-SCALE DATASETS; MAP-REDUCE; MEMORY OVERHEADS; MEMORY SAVINGS; REAL DATA SETS; SCALABILITY PROBLEMS; SCIENCE AND ENGINEERING; SPACE COMPLEXITY; SPECTRAL CLUSTERING; SUBSTANTIAL REDUCTION;

APPROXIMATION ALGORITHMS; LEARNING ALGORITHMS; LEARNING SYSTEMS; MATRIX ALGEBRA;

CLUSTERING ALGORITHMS;

EID: 84863889471     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2287076.2287111     Document Type: Conference Paper

References (40)

1. A. Anagnostopoulos, A. Dasgupta, and R. Kumar. Approximation algorithms for co-clustering. In In Proc. of Symposium on Principles of Database Systems (PODS'08), pages 201-210, Vancouver, BC, Canada, June 2008.
2. M. Charikar. Similarity estimation techniques from rounding algorithms. In In Proc. of ACM Symposium on Theory of Computing (STOC'02), pages 380-388, Montréal, Canada, May 2002.
3. W.-Y. Chen, Y. Song, H. Bai, C.-J. Lin, and E. Chang. Parallel spectral clustering in distributed systems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(3):568-586, March 2011.
4. O. Chum, J. Philbin, and A. Zisserman. Near duplicate image detection: min-hash and tf-idf weighting. In In Proc. of British Machine Vision Conference (BMVC'08), pages 25-31, Leeds, UK, September 2008.
5. N. Cristianini and J. Shawe-Taylor. An Introduction to Support Vector Machines and other Kernel-based Learning Methods. Cambridge University Press, 2000.
6. J. Cullum and R. Willoughby. Lanczos algorithms for large symmetric eigenvalue computations. IEEE Transactions on Information Theory, pages 43-49, 1985.
7. D. Davies and D. Bouldin. A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-1:224-227, 1979.
8. L. M. Delves and J. Walsh, editors. Numerical Solution of Integral Equations. Clarendon, Oxford, 1974.
9. P. Drineas and M. Mahoney. Approximating a gram matrix for improved kernel-based learning. In In Proc. of Annual Conference on Computational Learning Theory, pages 323-337, 2005.
10. X. Fern and C. Brodley. Random projection for high dimensional data clustering: a cluster ensemble approach. In In Proc. of International Conference on Machine Learning (ICML'03), pages 186-193, 2003.
11. B. Frey and D. Dueck. Clustering by passing messages between data points. Science, 315:972-976, 2007.
12. F. Gao. Distributed Approximate Spectral Clustering for Large-Scale Datasets. Master's thesis, Simon Fraser University, Canada, 2011.
13. J. A. Hartigan and M. A. Wong. A k-means clustering algorithm. Journal of the Royal Statistical Society, Series C (Applied Statistics), pages 22-29, 1979.
14. E. Hatcher and O. Gospodnetic. Lucene in Action. Manning Publications Co., Greenwich, CT, USA, 2004.
15. J. Hennessy and D. Patterson. Computer Architecture - A Quantitative Approach. Morgan Kaufmann, 2003.
16. A. K. Jain and R. C. Dubes. Algorithms for clustering data. Prentice-Hall, Inc., 1988.
17. U. Kang, C. Tsourakakis, and C. Faloutsos. Pegasus: A peta-scale graph mining system implementation and observations. In In Proc. of IEEE International Conference on Data Mining (ICDM'09), pages 229-238, Washington, DC, December 2009.
18. J. Kubica, J. Masiero, A. Moore, R. Jedicke, and A. Connolly. Variable kd-tree algorithms for efficient spatial pattern search. Technical Report CMU-RI-TR-05-43, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, September 2005.
19. R. B. Lehoucq, D. C. Sorensen, and C. Yang. Arpack users guide: Solution of large scale eigenvalue problems by implicitly restarted arnoldi methods, 1997.
20. J. Leskovec, L. A. Adamic, and B. A. Huberman. The dynamics of viral marketing. ACM Transactions on the Web, 1, May 2007.
21. J. Leskovec, D. Huttenlocher, and J. Kleinberg. Predicting positive and negative links in online social networks. In In Proc. of ACM Conference on World Wide Web (WWW'10), pages 641-650, April 2010.
22. J. Lin, D. Ryaboy, and K. Weil. Full-text indexing for optimizing selection operations in large-scale data analytics. In In Proc. of International Workshop on MapReduce and its Applications, pages 59-66, June 2011.
23. A. Matsunaga, M. Tsugawa, and J. Fortes. Cloudblast: Combining mapreduce and virtualization on distributed resources for bioinformatics applications. In In Proc. of IEEE International Conference on eScience, pages 222-229, Indianapolis, IN, December 2008.
24. T. Moon and W. Stirling. Mathematical methods and algorithms for signal processing. Prentice-Hall, Inc., 2000.
25. R. Motwani, A. Naor, and R. Panigrahi. Lower bounds on locality sensitive hashing. In In Proc. of Annual Symposium on Computational Geometry (SCG'06), pages 154-157, 2006.
26. S. Munder and D. Gavrila. An experimental study on pedestrian classification. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 28(11):1863-1868, Nov. 2006.
27. A. Y. Ng, M. I. Jordan, and Y. Weiss. On spectral clustering: Analysis and an algorithm. In Advances in Neural Information Processing Systems, pages 849-856. MIT Press, 2001.
28. J. Ohmer, F. Maire, and R. Brown. Implementation of kernel methods on the gpu. In In Proc. of Conference on Digital Image Computing: Techniques and Applications, page 78, Washington, DC, USA, December 2005.
29. S. Owen, R. Anil, T. Dunning, and E. Friedman. Mahout in Action. Manning Publications, 2011.
30. M. F. Porter. An Algorithm for Suffix Stripping. Program, 14(3):130-137, 1980.
31. B. Schlkopf, A. Smola, and K.-R. Müller. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10:1299-1319, July 1998.
32. J. Schuetter and T. Shi. Multi-sample data spectroscopic clustering of large datasets using Nystrom extension. Journal of Computational and Graphical Statistics, pages 531-542, 2011.
33. M. Seeger. Bayesian Model Selection for Support Vector Machines, Gaussian Processes and Other Kernel Classifiers, volume 12, pages 603-609. The MIT Press, 2000.
34. Y. Weiss. Segmentation using eigenvectors: A unifying view. In In Proc. of International Conference on Computer Vision, pages 975-982, 1999.
35. B. White, T. Yeh, J. Lin, and L. Davis. Web-scale computer vision using MapReduce for multimedia data mining. In In Proc. of ACM Workshop on Multimedia Data Mining, 2010.
36. C. K. I. Williams and M. Seeger. The effect of the input density distribution on kernel-based classifiers. In International Conference on Machine Learning, 2000.
37. C. K. I. Williams and M. Seeger. Using Nystrom method to speed up kernel machines, volume 13 of Advanced in Neural Information Processing Systems. MIT Press, 2001.
38. J. Yang and J.-Y. Yang. From image vector to matrix: a straightforward image projection technique - IMPCA vs. PCA. Pattern Recognition, 35:1997-1999, 2002.
39. D. Yogatama and K. Tanaka-Ishii. Multilingual spectral clustering using document similarity propagation. In In Proc. of Conference on Empirical Methods in Natural Language Processing, pages 871-879, 2009.
40. R. Zass and A. Shashua. Doubly stochastic normalization for spectral clustering. In Neural Information Processing Systems, pages 1569-1576, 2006.