Incremental tensor analysis: Theory and applications

ACM Transactions on Knowledge Discovery from Data

Volumn 2, Issue 3, 2008, Pages

  Sun, Jimeng ^a   Tao, Dacheng ^b   Papadimitriou, Spiros ^a   Yu, Philip S ^c,e   Faloutsos, Christos ^d,f  

^a IBM T J WATSON RESEARCH CENTER   (United States)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^c UNIVERSITY OF ILLINOIS AT CHICAGO   (United States)

^d CARNEGIE MELLON UNIVERSITY   (United States)

^e 1120 Science and Engineering Offices   (United States)

^f Carnegie Mellon University   (United States)

Author keywords

Multilinear algebra; Stream mining; Tensor

Indexed keywords

AND MODELS; APPROXIMATION ACCURACIES; CITATION NETWORKS; COMPUTATIONAL COSTS; CORRELATION STUDIES; DATA CUBES; DIMENSIONAL DATUMS; DIMENSIONALITY REDUCTIONS; EMPIRICAL STUDIES; FEATURE SELECTIONS; FUNDAMENTAL DESIGNS; GENERAL FRAMEWORKS; KEYWORD ASSOCIATIONS; LATENT SEMANTIC INDEXING; MATRIX DECOMPOSITIONS; MULTILINEAR ALGEBRA; NETWORK ANOMALY DETECTIONS; ORDER DATUMS; OUT-LIERS; PRINCIPAL COMPONENTS; REAL DATA SETS; REAL SETTINGS; RULE IDENTIFICATIONS; SCALE-UP; SENSOR MEASUREMENTS; SOCIAL NETWORKS; SPACE EFFICIENCIES; STREAM MINING; STREAMING DATUMS; TENSOR ANALYSIS; TIME DEPENDENCIES;

ALGEBRA; COMPUTATIONAL COMPLEXITY; CORRELATION METHODS; DYNAMIC ANALYSIS; INFORMATION THEORY; MATRIX ALGEBRA; PRINCIPAL COMPONENT ANALYSIS; SENSOR NETWORKS;

TENSORS;

EID: 55149108152     PISSN: 15564681     EISSN: 1556472X     Source Type: Journal    
DOI: 10.1145/1409620.1409621     Document Type: Article

References (46)

1. AGGARWAL, C. C., HAN, J., WANG, J., AND YU, P. S. 2003. A framework for clustering evolving data streams. In Proceedings of the 29th International Conference on Very Large Data Bases (VLDB). VLDB Endowment, 81-92.
2. AGRAWAL, R., IMIELIŃSKI, T., AND SWAMI, A. 1998. Mining association rules between sets of items in large databases. In Proceedings of the ACM SIGMOD International, Conference on Management of Data. ACM, New York, 207-216.
3. BRIN, S. AND PAGE, L. 1998. The anatomy of a large-scale hypertextual Web search engine. Comput. Netw. ISDN Syst. 30, 1-7, 107-117.
4. DING, C. AND YE, J. 2005. Two-Dimensional singular value decomposition (2dsvd) for 2d maps and images. In SDM.
5. DOMINGOS, P. AND HULTEN, G. 2000. Mining high-speed data streams. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). ACM, New York, 71-80.
6. DOMINGOS, P. AND RICHARDSON, M. 2001. Mining the network value of customers. In Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). ACM, New York, 57-66.
7. DRINEAS, P. AND MAHONEY, M. W. 2005. A randomized algorithm for a tensor-based generalization of the SVD. Tech. Rep. YALEU/DCS/TR-1327, Yale University.
8. ENRON DATASET. 2004. U.C. Berkeley Enron email analysis. http://bailando.sims.berkeley.edu/enron_email.html. FOLTZ, P. W. AND DUMAIS, S. T. 1992. Personalized information delivery: An analysis of information filtering methods. Commun. ACM 35, 12, 51-60.
9. GANTI, V., GEHRKE, J., AND RAMAKRISHNAN, R. 2002. Mining data streams under block evolution. SIGKDD Explor. 3, 2, 1-10.
10. GUHA, S., GUNOPULOS, D., AND KOUDAS, N. 2003. Correlating synchronous and asynchronous data streams. In Proceedings of the 9th ACM SIGKDD International, Conference on Knowledge Discovery and Data Mining (KDD). ACM, New York, 529-534.
11. GUHA, S., MEYERSON, A., MISHRA, N., MOTWANI, R., AND O'CALLAGHAN, L. 2003. Clustering data streams: Theory and practice. IEEE Trans. Knowl. Data Eng. 16, 3, 515-528.
12. HAYKIN, S. 1991. Adaptive Filter Theory, 2nd ed. Prentice-Hall, Upper Saddle River, NJ.
13. HE, X., CAI, D., AND NIYOGI, P. 2005. Tensor subspace analysis. In Proceeding of the Conference on Advance, in Neural Information Processing Systems (NIPS).
14. INDYK, P., KOUDAS, N., AND MUTHUKRISHNAN, S. 2000. Identifying representative trends in massive time series data sets using sketches. In Proceedings of the 26th International Conference on Very Large Data Bases (VLDB). Morgan Kaufmann, San Francisco, CA, 363-372.
15. JOLLIFFE, I. 2002. Principal Component Analysis .Springer.
16. KANNAN, R., VEMPALA, S., AND VETA, A. 2000. On clusterings-good, bad and spectral. In Proceedings of the 41st Annual Symposium on Foundations of Computer Science (FOCS). IEEE Computer Society, Washington, DC, 367.
17. KARYPIS, G. AND KUMAR, V. 1999. Parallel multilevel series k-way partitioning scheme for irregular graphs. SIAM Rev. 41, 2, 278-300.
18. KLEINBERG, J. M. 1999. Authoritative sources in a hyperlinked environment. J. ACM 46, 5, 604-632.
19. KOLDA, T. G. 2006. Multilinear operators for higher-order decompositions. Tech. Rep. SAND2006-2081, Sandia National Laboratory/April.
20. KOLDA, T. G. AND BADER, B. W. 2008 Tensor decompositions and applications. SIAM Rev. (in revision), http://csmr.ca.sandia.gov/~tgkolda/pubs/bibtgkfiles/SAND2007-6702.pdf.
21. KOLDA, T. G., BADER, B. W., AND KENNY, J. P. 2005. Higher-Order Web link analysis using multilinear algebra. In Proceedings of the 5th IEEE International Conference on Data Mining (ICDM). IEEE Computer Society, Washington, DC, 242-249.
22. KORN, F., LABRINIDIS, A., KOTIDIS, Y., AND FALOUTSOS, C. 2000. Quantifiable data-mining using ratio rules. The VLDB. 8, 3-4, 254-266.
23. KROONENBERG, P. AND LEEUW, J. D. 1980. Principal component analysis of three-mode data by means of alternating least square algorithms. Psychometrika 45.
24. KROONENBERG, P. M. 1983. Three-Mode Principal Component Analysis. Theory and Applications. DWO Press.
25. LATHAUWER, L. D., MOOR, B. D., AND VANDEWALLE, J. 2006. On the best rank-1 and rank-(r1,r2,. . .,rn) approximation of higher-order tensors. SIAM. Matrix Anal. Appl. 21.
26. MAHONEY, M. W., MAGGIONI, M., AND DRINEAS, P. 2006. Tensor-Cur decompositions for tensor-based data. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). ACM, New York, 327-336.
27. PAPADIMITRIOU, C. H., TAMAKI, H., RAGHAVAN, P., AND VEMPALA, S. 1998. Latent semantic indexing: A probabilistic analysis. In. Proceedings of the 17th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS). ACM, New York, 159-168.
28. PAPADIMITRIOU, S., BROCKWELL, A., AND FALOUTSOS, C. 2003. Adaptive, hands-off stream mining. In Proceedings of the 29th International Conference on Very Large Data Bases (VLDB). Endowment, 560-571.
29. PAPADIMITRIOU, S., SUN, J., AND FALOUTSOS, C. 2005. Streaming pattern discovery in multiple time-series. In Proceedings of the 31st International Conference on Very Large Data Bases (VLDB). Endowment, 697-708.
30. SAKURAI, Y., PAPADIMITRIOU, S., AND FALOUTSOS, C. 2005. Braid: Stream mining through group lag correlations. In Proceeding of the ACM /International Conference on Management of Data, 599-610.
31. SANGUANSAT, P., ASDORNWISED, W., JITAPUNKUL, S., AND MARUKATAT, S. 2006. Two-Dimensional linear discriminant analysis of principle component vectors for face recognition. IEICE - Trans. Inf. Syst. E89-D, 7, 2164-2170.
32. SHASHUA, A. AND LEVIN, A. 2001. Linear image coding for regression and classification using the tensor-rank principle. In Proceedings of the IEEE Conference on Computer vision and Patteroo Recongnistion CVPR (1). IEEE Computer Society, 42-49.
33. SMILDE, A., BRO, R., AND GELADI, P. 2004. Multi-Way Analysis: Applications in the Chemical Sciences, Wiley, West Sussex, UK.
34. SUN, J., PAPADIMITRIOU, S., AND YU, P. S. 2006a. Window-Based tensor analysis on high-dimensional and multi-aspect streams. In Proceedings of the 6th International Conference on Data Mining (ICDM). IEEE Computer Society, Washington, DC, 1076-1080.
35. SUN, J., TAO, D., AND FALOUTSOS, C. 2006a. Beyond streams and graphs: Dynamic tensor analysis. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). ACM, New York, 374-383.
36. TAO, D., LI, X., WU, X., AND MAYBANK, S. J. 2007. General tensor discriminant analysis and Gabor features for gait recognition. IEEE Trans. Pattern Anal. Mach. Intell. 29, 10, 1700-1715.
37. TUCKER, L. R. 1966. Some mathematical notes on three-mode factor analysis. Psychometriha, 31, 279-311.
38. UNIVERSITÄT TRIER. 2008. http://www.informatik. uni-trier.de/ ley/db/.
39. VAN LOAN, C. F. 2000. The ubiquitous Kronecker product. J. Comput. Appl. Math. 123, 85-100.
40. VASILESCU, M. A. O. AND TEHZOPOULOS, D. 2002. Multilinear analysis of image ensembles: Tensorfaces. In Proceedings of the 7th European Conference on Computer Vision-Part I (ECCV). Springer, London, UK, 447-460.
41. WANG, H., FAN, W., YU, P. S., AND HAN, J. 2003. Mining concept-drifting data streams using ensemble classifiers. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). ACM, New York, 226-235.
42. XU, D., YAN, S., ZHANG, L., ZHANG, H.-J., LIU, Z., AND SHUM, H.-Y. 2005. Concurrent subspaces analysis. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
43. YE, J. 2005. Generalized low rank approximations of matrices. Mach. Learn. 61, 1-8, 167-191.
44. YI, B.-K., SIDIROPOULOS, N., JOHNSON, T., JAGADISH, H., FALOUTSOS, C., AND BILIRIS, A. 2000. Online data-mining for co-evolving time sequences. In Proceedings of the 16th International Conference on Data Engineering (ICDE). IEEE Computer Society, Washington, DC, 13.
45. ZHAO, L. AND ZAKI, M. J. 2005. Tricluster: An effective algorithm for mining coherent clusters in 3D microarray data. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM, New York, 694-705.
46. ZHU, Y. AND SHASHA, D. 2002. Statstream: Statistical monitoring of thousands of data streams in real time. In Proceedings of the International Conference on Voy Large Databases (VLDB). 358-369.