On-line motif detection in time series with SwiftMotif

Pattern Recognition

Volumn 42, Issue 11, 2009, Pages 3015-3031

  Fuchs, Erich ^a   Gruber, Thiemo ^a   Nitschke, Jiri ^a   Sick, Bernhard ^a  

^a UNIVERSITY OF PASSAU   (Germany)

Author keywords

Motif detection; Orthogonal polynomials; Piecewise polynomial representation; Piecewise probabilistic representation; Polynomial approximation; Probabilistic modeling; Segmentation; SwiftMotif; Temporal data mining; Time series

Indexed keywords

MOTIF DETECTION; ORTHOGONAL POLYNOMIALS; PIECEWISE POLYNOMIAL REPRESENTATION; PIECEWISE PROBABILISTIC REPRESENTATION; PROBABILISTIC MODELING; SEGMENTATION; SWIFTMOTIF; TEMPORAL DATA MINING;

CLUSTERING ALGORITHMS; DATA MINING; FOURIER ANALYSIS; INFORMATION MANAGEMENT; LEAST SQUARES APPROXIMATIONS; MINING; NORMAL DISTRIBUTION; POLYNOMIAL APPROXIMATION;

TIME SERIES;

EID: 67649404577     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2009.05.004     Document Type: Article

References (52)

1. Yankov D., Keogh E., Medina J., Chiu B., and Zordan V. Detecting time series motifs under uniform scaling. Proceedings of the 13th International Conference on Knowledge Discovery and Data Mining (KDD), San Jose, CA, USA (2007) 844-853
2. Chiu B., Keogh E., and Lonardi S. Probabilistic discovery of time series motifs. Proceedings of the 9th International Conference on Knowledge Discovery and Data Mining (KDD), Washington, DC, USA (2003) 493-498
3. Tanaka Y., Iwamoto K., and Uehara K. Discovery of time series motif from multi-dimensional data based on MDL principle. Machine Learning vol. 58 (2005), Springer, Berlin 269-300
4. Das G., Lin K.-I., Mannila H., Renganathan G., and Smyth P. Rule discovery from time series. Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining (KDD), New York, NY, USA (1998) 16-22
5. Keogh E., Lin J., and Truppel W. Clustering of time series subsequences is meaningless: implications for previous and future research. Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM), Melbourne, FL, USA (2003) 115-122
6. A. Mueen, E. Keogh, Q. Zhu, S. Cash, B. Westover, Exact discovery of time series motifs, in: Proceedings of the SIAM International Conference on Data Mining (SDM), Sparks, NV, USA, 2009, pp. 473-484.
7. T.-C. Fu, F.-L. Chung, R. Luk, C.-M. Ng, Preventing meaningless stock time series pattern discovery by changing perceptually important point detection, in: Lecture Notes in Computer Science, Springer, vol. 3613, Berlin, Heidelberg, 2005, pp. 1171-1174.
8. Celly B., and Zordan V.B. Animated people textures. Proceedings of the 17th International Conference on Computer Animation and Social Agents (CASA), Geneva, Switzerland (2004)
9. M. Henzinger, P. Raghavan, S. Rajagopalan, Computing on data streams, Technical Report SRC-TN-1998-011, Digital Systems Research Center, Palo Alto, CA, USA, 1998.
10. Oates T., Schmill M.D., and Cohen P.R. A method for clustering the experiences of a mobile robot that accords with human judgments. Proceedings of the 17th National Conference on Artificial Intelligence (AAAI) and the 12th Conference on Innovative Applications of Artificial Intelligence (IAAI), Austin, TX, USA (2000) 846-851
11. Hamid R., Maddi S., Johnson A., Bobick A., Essa I., and Isbell C. Unsupervised activity discovery and characterization from event-streams. Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence (UAI), Edinburgh, Great Britain (2005) 251-258
12. Wilson W.O., Birkin P., and Aickelin U. The motif tracking algorithm. International Journal of Automation and Computing 5 1 (2008) 32-44
13. Androulakis I. New approaches for representing, analyzing and visualizing complex kinetic mechanisms. Proceedings of the 15th European Symposium on Computer Aided Process Engineering (ESCAPE), Barcelona, Spain (2005) 235-240
14. Androulakis I., Wu J., Vitoloand J., and Roth C. Selecting maximally informative genes to enable temporal expression profiling analysis. Proceedings of Foundations of Systems Biology in Engineering (FOSBE), Santa Barbara, CA, USA (2005)
15. Lin J., Keogh E., Lonardi S., and Patel P. Finding motifs in time series. Proceedings of the 2nd Workshop on Temporal Data Mining, Edmonton, Canada (2002) 53-68
16. Patel P., Keogh E., Lin J., and Lonardi S. Mining motifs in massive time series databases. Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM), Maebashi City, Japan (2002) 370-377
17. Murakami K., Doki S., Okuma S., and Yano Y. A study of extraction method of motion patterns observed frequently from time-series posture data. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC), Waikoloa, HI, USA (2005) 3610-3615
18. Tanaka Y., and Uehara K. Discover motifs in multidimensional time-series using the principal component analysis and the MDL principle. Proceedings of the International Conference on Machine Learning and Data Mining (MLDM), Tokushima, Japan (2003) 252-265
19. Minnen D., Starner T., Essa I., and Isbell C. Discovering characteristic actions from on-body sensor data. Proceedings of the 10th IEEE International Symposium on Wearable Computers (ISWC), Montreux, Switzerland (2006) 11-18
20. Minnen D., Isbell C.L., Essa I., and Starner T. Discovering multivariate motifs using subsequence density estimation and greedy mixture learning. Proceedings of the 22nd National Conference on Artificial Intelligence (AAAI), Vancouver, Canada (2007) 615-620
21. Keogh E., Chu S., Hart D., and Pazzani M. An online algorithm for segmenting time series. Proceedings of the 2001 IEEE International Conference on Data Mining (ICDM), San Jose, CA, USA (2001) 289-296
22. Keogh E., and Pazzani M. An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback. Proceedings of the 4th International Conference of Knowledge Discovery and Data Mining (KDD), New York, NY, USA (1998) 239-241
23. Lemire D. A better alternative to piecewise linear time series segmentation. Proceedings of the 20th National Conference on Artificial Intelligence (AAAI), Minneapolis, MN, USA (2007) 545-550
24. Keogh E.J., and Pazzani M.J. A simple dimensionality reduction technique for fast similarity search in large time series databases. Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications (PAKD), Kyoto, Japan (2000) 122-133
25. Palpanas T., Vlachos M., Keogh E., Gunopulos D., and Truppel W. Online amnesic approximation of streaming time series. Proceedings of the 20th International Conference on Data Engineering (ICDE), Boston, MA, USA (2004) 339-349
26. Keogh E., Chakrabarti K., Pazzani M., and Mehrotra S. Locally adaptive dimensionality reduction for indexing large time series databases. SIGMOD Record 30 2 (2001) 151-162
27. Keogh E., and Kasetty S. On the need for time series data mining benchmarks: a survey and empirical demonstration. Data Mining and Knowledge Discovery 7 4 (2003) 349-371
28. Pratt K.B., and Fink E. Search for patterns in compressed time series. International Journal of Image and Graphics 2 1 (2002) 89-106
29. Arita D., Yoshimatsu H., and Taniguchi R. Frequent motion pattern extraction for motion recognition in real-time human proxy. Proceedings of the International Workshop on Conversational Informatics (JSAI), Kitakyushu, Japan (2005) 25-30
30. Araki Y., Arita D., and Taniguchi R.-I. Motion motif extraction from high-dimensional motion information. Proceedings of the 12th Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV), Tokushima, Japan (2006) 92-97
31. Motro A., and Smets P. Uncertainty Management in Information Systems-From Needs to Solutions (1997), Springer, London, Great Britain
32. Bishop C.M. Pattern Recognition and Machine Learning (2006), Springer, New York, NY, USA
33. Golub G.H., and van Loan C.F. Matrix Computations, Johns Hopkins Studies in the Mathematical Sciences. third ed. (1996), Johns Hopkins University Press, Baltimore, London
34. Björck A. Numerical Methods for Least Squares Problems (1996), SIAM, Philadelphia, PA, USA
35. E. Fuchs, T. Gruber, J. Nitschke, B. Sick, On-line segmentation of time series based on polynomial least-squares approximations, IEEE Transactions on Pattern Analysis and Machine Intelligence, under review.
36. Elhay S., Golub G.H., and Kautsky J. Updating and downdating of orthogonal polynomials with data fitting applications. SIAM Journal on Matrix Analysis and Applications 12 2 (1991) 327-353
37. Fuchs E., and Donner K. Fast least-squares polynomial approximation in moving time windows. Proceedings of the 1997 International Conference on Acoustics, Speech and Signal Processing (ICASSP), Munich, Germany 3 (1997) 1965-1968
38. Fuchs E. On discrete polynomial least-squares approximation in moving time windows. In: Gautschi W., Golub G., and Opfer G. (Eds). Applications and Computation of Orthogonal Polynomials, International Series of Numerical Mathematics, vol. 131, Birkhäuser, Basel, Switzerland (Proceedings of the Conference at the Mathematical Research Institute Oberwolfach, Germany, March 22-28, 1998) (1999) 93-107
39. Liu H., and Motoda H. Feature Selection for Knowledge Discovery and Data Mining (1998), Kluwer Academic Publishers, Boston
40. Liu H., and Motoda H. Feature Extraction, Construction, and Selection: A Data Mining Perspective (1998), Kluwer Academic Publishers, Boston
41. Duda R.O., Hart P.E., and Stork D.G. Pattern Classification. second ed. (2001), Wiley-Interscience, New York
42. Dunn J.C. Well separated clusters and optimal fuzzy partitions. Journal of Cybernetics 4 (1974) 95-104
43. Davies D., and Bouldin D. A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence 1 (1979) 224-227
44. L. Prechelt, Proben1-a set of neural network benchmark problems and benchmarking rules, Technical Report 21/94, Universität Karlsruhe, Fakultät für Informatik, Karlsruhe, Germany, 1994.
45. E. Keogh, Time series classification/clustering. URL: 〈http://www.cs.ucr.edu/eamonn/time_series_data/〉.
46. R. Klinge, Das Elektrokardiogramm, Thieme, Stuttgart, Germany, 2002.
47. E. Keogh, X. Xi, L. Wei, C.A. Ratanamahatana, The UCR time series classification/clustering homepage, 2006. URL: 〈www.cs.ucr.edu/eamonn/time_series_data/〉.
48. Ding H., Trajcevski G., Scheuermann P., Wang X., and Keogh E. Querying and mining of time series data: experimental comparison of representations and distance measures. Proceedings of the 34th International Conference on Very Large Data Bases (VLDB08), Auckland, New Zealand (2008) 1542-1552
49. Fukunaga K. Introduction to Statistical Pattern Recognition. second ed. (1990), Academic Press, New York, NY, USA
50. Lorenz E.N. Deterministic nonperiodic flow. Journal of the Atmospheric Sciences 20 2 (1963) 130-141
51. Tucker W. A rigorous ODE solver and Smale's 14th problem. Foundations of Computational Mathematics 2 1 (2002) 53-117
52. D. Fisch, T. Gruber, B. Sick, SwiftRule: mining comprehensible classification rules for time series analysis, IEEE Transactions on Knowledge and Data Engineering, under review.