Towards never-ending learning from time series streams

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn Part F128815, Issue , 2013, Pages 874-882

  Hao, Yuan ^a   Chen, Yanping ^a   Zakaria, Jesin ^b   Hu, Bing ^b   Rakthanmanon, Thanawin ^c   Keogh, Eamonn ^b  

^a Department of Computer Science and Engineering ^*  (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c KASETSART UNIVERSITY   (Thailand)

Author keywords

Classification; Data streams; Never ending learning; Time series

Indexed keywords

BEHAVIORAL RESEARCH; CLASSIFICATION (OF INFORMATION); DATA MINING; TEACHING; TIME SERIES;

DATA MINING COMMUNITY; DATA STREAM; HUMAN BEHAVIOR ANALYSIS; LEARNING FRAMEWORKS; NEVER-ENDING LEARNING; SCIENTIFIC APPLICATIONS; TIME SERIES CLASSIFICATIONS; WILDLIFE MONITORING;

EDUCATION;

EID: 85021230208     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2487575.2487634     Document Type: Conference Paper

References (24)

1. E. Achtert, C. Bohm, H-P. Kriegel, P. Kröger. Online Hierarchical Clustering in a Data Warehouse Environment Data Mining. ICDM, 2005, pp.10-17.
2. G. Batista, E. Keogh, A. Mafra-Neto, E. Rowton: Sensors and software to allow computational entomology, an emerging application of data mining. KDD 2011: 761-764.
3. E. Berlin and K. Laerhoven. Detecting leisure activities with dense motif discovery. Proceedings of the 2012 Intl Conference on Uniquitous Computing. pp. 250-59.
4. M. Borazio and K. Laerhoven. Combining Wearable and Environmental Sensing into an Unobtrusive Tool for Long-Term Sleep Studies. 2nd ACM SIGHIT 2012.
5. A.S.L.O. Campanharo, M.I. Sirer, R.D. Malgren, F.M. Ramos, L.A.N Nunes. Duality between time series and networks. Plos One, 6 (2011), p. e23378.
6. A. Carlson, J. Betteridge, B. Kisiel, B. Settles, E.R. Hruschka Jr. and T.M. Mitchell. Toward an Architecture for Never-Ending Language Learning. In Proc' AAAI, 2010.
7. G. Cormode, M. Hadjieleftheriou. Methods for finding frequent items in data streams. VLDB J. 19(1): 3-20 (2010).
8. H. Ding, G. Trajcevski, P. Scheuermann, X. Wang, E. J. Keogh. Querying and mining of time series data. Experimental comparison of representations and distance measures. PVLDB 1(2): 1542-1552 (2008).
9. C. Elkan and K. Noto: Learning classifiers from only positive and unlabeled data. KDD 2008: pp 213-220.
10. F. Ferreira, D. Bota, A. Bross, C. Mélot, J Vincent. Serial evaluation of the SOFA score to predict outcome in critically ill patients. JAMA 2001 Oct 10; 286(14):1754-8.
11. A. Goldberger et al. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23):pp 215-20 2000.
12. A. Goldberger et al. Physionet, accessed Feb-04-2013 www.physionet.ph.biu.ac.il/physiobank/database/chfdb/.
13. Y. Hao project webpage: Www.cs.ucr.edu/yhao/NELTS.html.
14. B. Hu, Y. Chen and E. J. Keogh. Time Series Classification under More Realistic Assumptions. SDM 2013.
15. S. Jin, Z. Chen, E. Backus, X. Sun, B. Xiao. 2012. Characterization of EPG waveforms for the tea green leafhopper on tea plants and their correlation with stylet activities. Journal of Insect Physiology. 58:1235-1244.
16. K. Jones (2012). www.batdetective.org.
17. L. Mitchell. Time Segregated Mosquito Collections with a CDC Miniature Light Trap. Mosquito News 1981. 42: 12.
18. A. Mueen, E. J. Keogh, N. Young: Logical-shapelets: An expressive primitive for time series classification. KDD 2011: 1154-62.
19. S. Nassar, J. Sander, C. Cheng: Incremental and Effective Data Summarization for Dynamic Hierarchical Clustering. SIGMOD Conference 2004: 467-478.
20. B. Settles. Active Learning. Morgan & Claypool, 2012.
21. M. Stikic, T. Huynh, K. V. Laerhoven and B. Schiele. ADL Recognition Based on the Combination of RFID and Accelerometer Sensing. PervasiveHealth, 2008. pp. 258-263.
22. C. J. Van Rijsbergen, Information Retrieval, 2nd edition, London, England: Butterworths, 1979.
23. D. Roggen et al. Collecting complex activity data sets in highly rich networked sensor environments, In Proc' 7th IEEE INSS (2010), pp. 233-240.
24. A. Veeraraghavan, R. Chellappa, A. K. Roy-Chowdhury, The Function Space of an Activity, Computer Vision and Pattern Recognition, 2006.