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Towards Perception Based Time Series Data Mining

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Forging New Frontiers: Fuzzy Pioneers I

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 217))

Abstract

Human decision making procedures in problems related with analysis of time series data bases (TSDB) often use perceptions like “several days”, “high price”, “quickly increasing” etc. Computing with Words and Perceptions can be used to formalize perception based expert knowledge and inference mechanisms defined on numerical domains of TSDB. For extraction from TSDB perception based information relevant to decision making problems it is necessary to develop methods of perception based time series data mining (PTSDM). The paper considers different approaches used in analysis of time series databases for description of perception based patterns and discusses some methods of PTSDM.

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References

  1. Agrawal, R., Psaila, G., Wimmers, E.L., Zait M.: Querying shapes of histories. Proc. 21st Intern. Conf. Very Large Databases, VLDB ’95, Zurich, Switzerland (1995) 502–514

    Google Scholar 

  2. Allen, J.F.: Maintaining knowledge about temporal intervals. Comm. ACM. 26 (11) (1983) 832–843

    Article  MATH  Google Scholar 

  3. Baldwin, J.F., Martin, T.P., Rossiter, J.M.: Time series modeling and prediction using fuzzy trend information. Proc. Int. Conf. SC Information/Intelligent Syst. (1998) 499–502

    Google Scholar 

  4. Batyrshin, I.: Construction of granular derivatives and solution of granular initial value problem. In: Nikravesh, M.; Zadeh, L.A.; Korotkikh, V. (eds.). Fuzzy Partial Differential Equations and Relational Equations. Reservoir Characterization and Modeling. Studies in Fuzziness and Soft Computing, Vol. 142. Springer-Verlag, Berlin Heidelberg New York (2004) 285–307

    Google Scholar 

  5. Batyrshin, I., Cosultchi, A., Herrera-Avelar, R.: Association network of petroleum wells based on partial time series analysis. Proc. Workshop Intel. Comput., Mexico (2004) 17–26

    Google Scholar 

  6. Batyrshin, I., Herrera-Avelar, R., Sheremetov, L., Panova, A.: Association networks in time series data mining. NAFIPS 2005, USA (2005) 754–759

    Google Scholar 

  7. Batyrshin, I., Herrera-Avelar, R., Sheremetov, L., Suarez, R.: On qualitative description of time series based on moving approximations. Proc. Intern. Conf. Fuzzy Sets and Soft Computing in Economics and Finance, FSSCEF 2004, Russia, vol. I, (2004) 73–80

    Google Scholar 

  8. Batyrshin, I., Herrera-Avelar, R., Sheremetov, L., Suarez, R.: Moving approximations in time series data mining. Proc. Int. Conf. Fuzzy Sets and Soft Computing in Economics and Finance FSSCEF 2004, St. Petersburg, Russia, v. I, (2004) 62–72

    Google Scholar 

  9. Batyrshin, I., Wagenknecht, M.: Towards a linguistic description of dependencies in data. Int. J. Applied Math. and Computer Science. Vol. 12 (2002) 391–401

    Google Scholar 

  10. Batyrshin, I.Z.: On reconstruction of perception based functions with convex-concave patterns. Proc. Int. Conf. Computational Intelligence ICCI 2004, Nicosia, North Cyprus, Near East University Press (2004) 30–34

    Google Scholar 

  11. Bezdek, J. C.: Fuzzy models and digital signal processing (for pattern recognition): Is this a good marriage? Digital Signal Processing, 3 (1993) 253–270

    Article  Google Scholar 

  12. Bowerman, B.L., O’Connell, R.T.: Time series and forecasting: an applied approach. Mass. Duxbury Press (1979)

    MATH  Google Scholar 

  13. Boyd, S.: TREND: A system for generating intelligent descriptions of time-series data. Proc. IEEE Intern. Conf. Intelligent Processing Systems (ICIPS1998) (1998)

    Google Scholar 

  14. Chen, G.Q., Wei, Q., Kerre E.E.: Fuzzy Data Mining: Discovery of Fuzzy General. Assoc. Rules. In: Recent Research Issues Manag. Fuzz. in DB, Physica-Verlag (2000)

    Google Scholar 

  15. Cheung, J.T., Stephanopoulos, G.: Representation of process trends. -Part I. A formal representation framework. Computers and Chemical Engineering, 14 (1990) 495–510

    Article  Google Scholar 

  16. Das, G., Lin, K.I., Mannila, H., Renganathan, G., Smyth, P.: Rule discovery from time series. Proc. KDD98 (1998) 16–22

    Google Scholar 

  17. Dubois, D., Hullermeier, E., Prade, H.: A note on quality measures for fuzzy association rules. IFSA 2003, LNAI 2715, Springer-Verlag (2003) 677–648

    Google Scholar 

  18. Hand, D., Manilla, H., Smyth, P.: Principles of Data Mining. MIT Press, Cambridge (2001)

    Google Scholar 

  19. Höppner, F.: Learning Temporal Rules from State Sequences. IJCAI Workshop on Learning from Temporal and Spatial Data, Seattle, USA (2001) 25–31

    Google Scholar 

  20. Jang, J.-S.R., Sun, C.T., Mizutani, E.: Neuro-Fuzzy and Soft Computing. A Computational Approach to Learning and Machine Intelligence. Prentice-Hall International (1997)

    Google Scholar 

  21. Kacprzyk, J., Zadrozny, S.: Data Mining via Linguistic Summaries of Data: An Interactive Approach, IIZUKA’98, Iizuka, Japan (1998) 668–671

    Google Scholar 

  22. Kandel, A., Last, M., Bunke, H. (eds): Data Mining and Computational Intelligence, Physica-Verlag, Studies in Fuzziness and Soft Computing, Vol. 68. (2001)

    Google Scholar 

  23. KDnuggets: Polls: Time-Series Data Mining (Nov 2004). What Types of TSDM You’ve Done? http://www.kdnuggets.com/polls/2004/time_series_data_mining.htm

    Google Scholar 

  24. Klir, G.J., Clair, U.S., Yuan, B.: Fuzzy Set Theory: Foundations and Applications, Prentice Hall Inc. (1997)

    Google Scholar 

  25. Konstantinov, K.B., Yoshida, T.: Real-time qualitative analysis of the temporal shapes of (bio) process variables. American Inst. Chem. Eng. Journal 38 (1992) 1703–1715

    Google Scholar 

  26. Kukich, K.: Design of a knowledge-based report generator. Proc. 21st Annual Meeting of the Association for Computational Linguistics (ACL-1983) (1983) 145–150

    Google Scholar 

  27. Last, M., Klein, Y., Kandel A.: Knowledge discovery in time series databases. IEEE Trans. SMC, Part B, 31 (2001) 160–169

    Article  Google Scholar 

  28. Lin, J., Keogh, E., Lonardi, S., Chiu, B.: A symbolic representation of time series, with implications for streaming algorithms. Proc. 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery. San Diego, CA. (2003)

    Google Scholar 

  29. Maner, W., Joyce, S.: WXSYS: Weather Lore + Fuzzy Logic = Weather Forecasts. Present. 1997 CLIPS Virtual Conf. http://web.cs.bgsu.edu/maner/wxsys/wxsys.htm (1997)

    Google Scholar 

  30. Mitra, S., Pal, S.K., Mitra, P.: Data Mining in Soft Computing Framework: A Survey. IEEE Transactions Neural Networks, 13 (2002) 3–14

    Google Scholar 

  31. Pons, O., Vila, M.A., Kacprzyk J. (eds.): Knowledge Management in Fuzzy Databases, Physica-Verlag (2000)

    Google Scholar 

  32. Stockman, G., Kanal, L., Kyle, M.C.: Structural pattern recognition of carotid pulse waves using a general waveform parsing system. CACM 19 (1976) 688–695

    MATH  Google Scholar 

  33. StockReporter. http://www.ics.mq.edu.au/ ltgdemo/StockReporter/about.html

    Google Scholar 

  34. Sudkamp, T. Examples, counterexamples, and measuring fuzzy associations. Fuzzy Sets and Systems, 149 (2005) 57–71

    Article  MATH  MathSciNet  Google Scholar 

  35. Yager, R.R.: On linguistic summaries of data. In: Piatetsky-Shapiro, G., Frawley, B. (eds.): Knowledge Discovery in Databases, MIT Press, Cambridge, MA (1991) 347–363

    Google Scholar 

  36. Yu, J., Reiter, E., Hunter, J., Mellish C.: Choosing the content of textual summaries of large time-series data sets. Natural Language Engineering (2005) (To appear)

    Google Scholar 

  37. Zadeh, L.A.: Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Systems, Man and Cybernetics SMC-3 (1973) 28–44

    Google Scholar 

  38. Zadeh, L.A.: From computing with numbers to computing with words - from manipulation of measurements to manipulation of perceptions. IEEE Trans. on Circuits and Systems - 1: Fundamental Theory and Applications, vol. 45, (1999) 105–119

    Google Scholar 

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Authors and Affiliations

  1. Research Program in Applied Mathematics and Computing (PIMAyC) Mexican Petroleum Institute, Eje Central Lazaro Cardenas 152, Col. San Bartolo Atepehuacan, C.P. 07730, Mexico

    Ildar Z. Batyrshin & Leonid Sheremetov

Authors
  1. Ildar Z. Batyrshin
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  2. Leonid Sheremetov
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Editor information

Editors and Affiliations

  1. Department of Electrical Engineering and Computer Science – EECS, University of California Berkeley, 94720 Berkeley, USA

    Masoud Nikravesh

  2. PAN Warszawa Systems Research Institute, Newelska 6, 01-447 Warszawa, Poland

    Janusz Kacprzyk

  3. Department of Electrical Engineering and Computer Science(EECS), University of California Berkeley, Soda Hall 729, 94720-1776 Berkeley, USA

    Lofti A. Zadeh

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Batyrshin, I.Z., Sheremetov, L. (2007). Towards Perception Based Time Series Data Mining. In: Nikravesh, M., Kacprzyk, J., Zadeh, L.A. (eds) Forging New Frontiers: Fuzzy Pioneers I. Studies in Fuzziness and Soft Computing, vol 217. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73182-5_11

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  • DOI: https://doi.org/10.1007/978-3-540-73182-5_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73181-8

  • Online ISBN: 978-3-540-73182-5

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