Event-based classification of social media streams

Proceedings of the 2nd ACM International Conference on Multimedia Retrieval, ICMR 2012

Volumn , Issue , 2012, Pages

  Reuter, Timo ^a   Cimiano, Philipp ^a  

^a BIELEFELD UNIVERSITY   (Germany)

Author keywords

Classification; Clustering; Event identification; SVMs

Indexed keywords

CLUSTERING; DATA ITEMS; DATA POINTS; DATA SIZE; EVENT DETECTION; EVENT IDENTIFICATION; EVENT-BASED; MACHINE LEARNING TECHNIQUES; SOCIAL MEDIA; STATE-OF-THE-ART APPROACH; SVMS; USER INTERVENTION;

CLASSIFICATION (OF INFORMATION);

EID: 84864140773     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2324796.2324824     Document Type: Conference Paper

References (37)

1. C. Aggarwal and P. Yu. Outlier detection for high dimensional data. ACM Sigmod Record, 30(2):37-46, 2001.
2. J. Allan, R. Papka, and V. Lavrenko. On-line new event detection and tracking. In Proceedings of SIGIR, pages 37-45, 1998.
3. D. Barbará, Y. Li, and J. Couto. Coolcat: an entropy-based algorithm for categorical clustering. In Proceedings of CIKM, pages 582-589, 2002.
4. H. Becker, M. Naaman, and L. Gravano. Learning similarity metrics for event identification in social media. In Proceedings of WSDM, pages 291-300, 2010.
5. G. Bejerano and G. Yona. Variations on probabilistic suffix trees: statistical modeling and prediction of protein families. Bioinformatics, 17(1):23-43, 2001. (Pubitemid 32178011)
6. S. Budalakoti, A. Srivastava, R. Akella, and E. Turkov. Anomaly detection in large sets of high-dimensional symbol sequences. NASA ARC, Tech. Rep. NASA TM-2006-214553, 2006.
7. P. Chan, M. Mahoney, and M. Arshad. A machine learning approach to anomaly detection. Department of Computer Sciences, Florida Institute of Technology, Melbourne, 2003.
8. V. Chandola, A. Banerjee, and V. Kumar. Anomaly detection: A survey. ACM Computing Surveys (CSUR), 41(3):15, 2009.
9. L. Chen and A. Roy. Event detection from ickr data through wavelet-based spatial analysis. In In Proceedings CIKM, 2009.
10. Q. Ding, Q. Ding, and W. Perrizo. Decision tree classification of spatial data streams using peano count trees. Proceedings of the ACM Symposium on Applied Computing, 2002.
11. P. Domingos and G. Hulten. Mining high-speed data streams. In Proceedings of KDD, pages 71-80. ACM, 2000.
12. E. Eskin. Anomaly detection over noisy data using learned probability distributions. In Proceedings of ICML, 2000.
13. E. Eskin, A. Arnold, M. Prerau, L. Portnoy, and S. Stolfo. A geometric framework for unsupervised anomaly detection. Applications of Data Mining in Computer Security, 6:77-102, 2002.
14. E. Eskin, W. Lee, and S. Stolfo. Modeling system calls for intrusion detection with dynamic window sizes. In DARPA Information Survivability Conference & Exposition II, 2001. DISCEX'01. Proceedings, volume 1, pages 165-175. IEEE, 2001.
15. M. Ester, H. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of KDD, pages 226-231, 1996.
16. T. Fawcett and F. Provost. Activity monitoring: Noticing interesting changes in behavior. In Proceedings of KDD, pages 53-62. ACM, 1999.
17. J. Gao, W. Fan, J. Han, and P. Yu. A general framework for mining concept-drifting data streams with skewed distributions. Proc. of SDM'07, 2007.
18. S. Guha, R. Rastogi, and K. Shim. Rock: A robust clustering algorithm for categorical attributes. Information systems, 25(5):345-366, 2000.
19. M. Last. Online classification of nonstationary data streams. Intelligent Data Analysis, 6(2):129-147, 2002.
20. X. Liu, R. Troncy, and B. Huet. Finding media illustrating events. In Proceedings of ICMR'11, pages 1-8, 2011.
21. M. Markou and S. Singh. Novelty detection: a review-part 1: statistical approaches. Signal Processing, 83(12):2481-2497, 2003.
22. M. Markou and S. Singh. Novelty detection: a review|part 2:: neural network based approaches. Signal Processing, 83(12):2499-2521, 2003.
23. R. Mattivi, G. Boato, and B. G. D. Natale. Event-based media organization and indexing. Infocummunications, (3):99-18, 2011.
24. J. Platt et al. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in large margin classifiers, 10(3):61-74, 1999.
25. G. Ratsch, S. Mika, B. Scholkopf, and K. Muller. Constructing boosting algorithms from svms: an application to one-class classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(9):1184-1199, 2002. (Pubitemid 35242580)
26. T. Rattenbury, N. Good, and M. Naaman. Towards automatic extraction of event and place semantics from ickr tags. In Proceedings of SIGIR, pages 103-110, 2007. (Pubitemid 350164950)
27. S. Rendle and L. Schmidt-Thieme. L.: Scaling record linkage to non-uniform distributed class sizes. In Proceedings of the 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2008.
28. T. Reuter and P. Cimiano. Learning similarity functions for event identification using support vector machines. In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (KDIR), 2011.
29. T. Reuter, P. Cimiano, L. Drumond, K. Buza, and L. Schmidt-Thieme. Scalable event-based clustering of social media via record linkage techniques. In Fifth International AAAI Conference on Weblogs and Social Media (ICWSM), 2011.
30. S. Robertson and K. Jones. Relevance weighting of search terms. Journal of the American Society for Information science, 27(3):129-146, 1976.
31. H. Sayyadi, M. Hurst, and A. Maykov. Event detection and tracking in social streams. In In Proceedings of the International Conference on Weblogs and Social Media (ICWSM), 2009.
32. C. Surace and K. Worden. A novelty detection method to diagnose damage in structures: an application to an offshore platform. In Proceedings of the International Offshore and Polar Engineering Conference, number 8, pages 64-70, 1998.
33. D. Theofilou, V. Steuber, and E. Schutter. Novelty detection in a kohonen-like network with a long-term depression learning rule. Neurocomputing, 52:411-417, 2003. (Pubitemid 36700538)
34. A. Vinueza and G. Grudic. Unsupervised outlier detection and semi-supervised learning. Technical report, Technical Report CU-CS-976-04, University of Colorado at Boulder, 2004.
35. H. Wang, W. Fan, P. Yu, and J. Han. Mining concept-drifting data streams using ensemble classifiers. In Proceedings of KDD, pages 226-235. ACM, 2003.
36. N. Ye and Q. Chen. An anomaly detection technique based on a chi-square statistic for detecting intrusions into information systems. Quality and Reliability Engineering International, 17(2):105-112, 2001. (Pubitemid 32443869)
37. D. Yu, G. Sheikholeslami, and A. Zhang. Findout: finding outliers in very large datasets. Knowledge and Information Systems, 4(4):387-412, 2002.