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Temporal Evolution and Local Patterns

  • Conference paper
Local Pattern Detection

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3539))

Abstract

We elaborate on the subject of pattern change as a result of population evolution. We provide an overview of literature threads relevant to this subject, where the focus is on related works in the area of pattern adaptation rather than on modelling or understanding change. We then describe our temporal model for patterns as evolving objects and propose criteria to capture the interestingness of pattern change. We also present heuristics that trace interesting changes.

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References

  1. Abrahams, T., Roddick, J.: Incremental meta-mining from large temporal data sets. In: Kambayashi, Y., Lee, D.-L., Lim, E.-p., Mohania, M., Masunaga, Y. (eds.) ER Workshops 1998. LNCS, vol. 1552, pp. 41–55. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  2. Adomavicius, G., Tuzhilin, A.: Discovery of actionable patterns in databases: The action hierarchy approach. In: KDD, Newport Beach, CA, August 1997, pp. 111–114 (1997)

    Google Scholar 

  3. Aslam, J., Pelekhov, K., Rus, D.: A practical clustering algorithm for static and dynamic information organization. In: SODA: ACM-SIAM Symposium on Discrete Algorithms (A Conference on Theoretical and Experimental Analysis of Discrete Algorithms), January 1999, pp. 51–60 (1999)

    Google Scholar 

  4. Baron, S.: Temporale Aspekte entdeckten Wissens: Ein Bezugssystem für die Evolution von Mustern. PhD thesis, Humboldt University Berlin (2004); English title: ”Temporal Aspects of Discovered Knowledge: A Framework for Pattern Evolution” (On German)

    Google Scholar 

  5. Baron, S., Spiliopoulou, M.: Monitoring change in mining results. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds.) DaWaK 2001. LNCS, vol. 2114, p. 51. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  6. Baron, S., Spiliopoulou, M.: Monitoring the results of the KDD process: An overview of pattern evolution. In: Meij, J.M. (ed.) Dealing with the Data Flood: Mining data, text and multimedia, ch. 6, STT Netherlands Study Center for Technology Trends, The Hague, Netherlands, April 2002, pp. 845–863 (2002)

    Google Scholar 

  7. Baron, S., Spiliopoulou, M.: Monitoring the evolution of web usage patterns. In: Berendt, B., Hotho, A., Mladenič, D., van Someren, M., Spiliopoulou, M., Stumme, G. (eds.) EWMF 2003. LNCS (LNAI), vol. 3209, pp. 181–200. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  8. Baron, S., Spiliopoulou, M., Günther, O.: Efficient monitoring of patterns in data mining environments. In: Kalinichenko, L.A., Manthey, R., Thalheim, B., Wloka, U. (eds.) ADBIS 2003. LNCS, vol. 2798, pp. 253–265. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  9. Berger, G., Tuzhilin, A.: Discovering unexpected patterns in temporal data using temporal logic. In: Etzion, O., Jajodia, S., Sripada, S. (eds.) Dagstuhl Seminar 1997. LNCS, vol. 1399, pp. 281–309. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  10. Case, J., Jain, S., Kaufmann, S., Sharma, A., Stephan, F.: Predictive Learning Models for Concept Drift. Theoretical Computer Science 268(2), 323–349 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  11. Chakrabarti, S., Sarawagi, S., Dom, B.: Mining Surprising Patterns Using Temporal Description Length. In: Gupta, A., Shmueli, O., Widom, J. (eds.) VLDB 1998, New York City, NY, August 1998, pp. 606–617. Morgan Kaufmann, San Francisco (1998)

    Google Scholar 

  12. Chen, X., Petrounias, I.: Mining temporal features in association rules. In: Żytkow, J.M., Rauch, J. (eds.) PKDD 1999. LNCS (LNAI), vol. 1704, pp. 295–300. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  13. Ester, M., Kriegel, H.-P., Sander, J., Wimmer, M., Xu, X.: Incremental Clustering for Mining in a Data Warehousing Environment. In: Proceedings of the 24th International Conference on Very Large Data Bases, New York City, New York, USA, August 1998, pp. 323–333. Morgan Kaufmann, San Francisco (1998)

    Google Scholar 

  14. Fan, W.: Systematic Data Selection to Mine Concept-Drifting Data Streams. In: Proc. of 10th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD 2004), Seattle, Washington, USA, August 2004, pp. 128–137. ACM Press, New York (2004)

    Chapter  Google Scholar 

  15. Freitas, A.A.: On Objective Measures of Rule Surprisingness. In: Żytkow, J.M. (ed.) PKDD 1998. LNCS, vol. 1510. Springer, Heidelberg (1998)

    Google Scholar 

  16. Ganti, V., Gehrke, J., Ramakrishnan, R.: A Framework for Measuring Changes in Data Characteristics. In: Proceedings of the 18th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, Philadelphia, Pennsylvania, May 1999, pp. 126–137. ACM Press, New York (1999)

    Google Scholar 

  17. Ganti, V., Gehrke, J., Ramakrishnan, R.: CACTUS: Clustering categorical data using summaries. In: Proc. of 5th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD 1999), San Diego, CA, August 1999, pp. 73–83. ACM Press, New York (1999)

    Chapter  Google Scholar 

  18. Ganti, V., Gehrke, J., Ramakrishnan, R.: DEMON: Mining and Monitoring Evolving Data. In: Proceedings of the 15th International Conference on Data Engineering, San Diego, California, USA, February 2000, pp. 439–448. IEEE Computer Society, Los Alamitos (2000)

    Google Scholar 

  19. Grieser, G.: Hypothesis assessments as guidance for incremental and metalearning. In: Keller, J., Girard-Carrier, C. (eds.) Proc. 11th European Conference on Machine Learning, Workshop on Meta Learning: Building Automatic Advice Strategies for Model Selection and Method Combination, May 2000, pp. 97–108 (2000)

    Google Scholar 

  20. Hulten, G., Spencer, L., Domingos, P.: Mining Time-Changing Data Streams. In: Provost, F., Srikant, R. (eds.) Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2001, pp. 97–106. ACM Press, New York (2001)

    Chapter  Google Scholar 

  21. Karimi, K., Hamilton, H.J.: Distinguishing causal and acausal temporal relations. In: Whang, K.-Y., Jeon, J., Shim, K., Srivastava, J. (eds.) PAKDD 2003. LNCS (LNAI), vol. 2637, pp. 234–240. Springer, Heidelberg (2003)

    Google Scholar 

  22. Klinkenberg, R., Joachims, T.: Detecting Concept Drift with Support Vector Machines. In: Langley, P. (ed.) Proceedings of the 17th International Conference on Machine Learning, Stanford, USA, pp. 487–494. Morgan Kaufmann Publishers, San Francisco (2000)

    Google Scholar 

  23. Klinkenberg, R., Renz, I.: Adaptive Information Filtering: Learning in the Presence of Concept Drift. In: Workshop on Learning for Text Categorization at 15th National Conference on Artificial Intelligence, AAAI 1998 (1998)

    Google Scholar 

  24. Koychev, I.: Tracking changing user interests through prior-learning of context. LNCS, vol. 2347, p. 223. Springer, Heidelberg (2002)

    Google Scholar 

  25. Lau, A., Ong, S.S., Mahidadia, A., Hoffmann, A., Westbrook, J., Zrimec, T.: Mining patterns of dyspepsia symptoms across time points using constraint association rules. In: Whang, K.-Y., Jeon, J., Shim, K., Srivastava, J. (eds.) PAKDD 2003. LNCS (LNAI), vol. 2637, pp. 124–135. Springer, Heidelberg (2003)

    Google Scholar 

  26. Lee, S.D., Cheung, D.W.-L.: Maintenance of Discovered Association Rules: When to update? In: ACM-SIGMOD Workshop on Data Mining and Knowledge Discovery (DMKD 1997), Tucson, Arizona (May 1997)

    Google Scholar 

  27. Lee, S.D., Cheung, D.W., Kao, B.: Is Sampling Useful in Data Mining? A Case in the Maintenance of Discovered Association Rules. Data Mining and Knowledge Discovery 2(3), 233–262 (September 1998)

    Article  Google Scholar 

  28. Liu, B., Hsu, W., Chen, S.: Using General Impressions to Analyze Discovered Classification Rules. In: Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, Newport Beach, USA, August 1997, pp. 31–36. AAAI Press, Menlo Park (1997)

    Google Scholar 

  29. Liu, B., Ma, Y., Lee, R.: Analyzing the interestingness of association rules from the temporal dimension. In: IEEE International Conference on Data Mining (ICDM 2001), Silicon Valley, USA, November 2001, pp. 377–384 (2001)

    Google Scholar 

  30. Morik, K., Köpcke, H.: Features for Learning Local Patterns in Time-Stamped Data. In: Morik, K., Boulicaut, J.-F., Siebes, A. (eds.) Detecting Local Patterns. LNCS. Springer, Heidelberg (2005) (to appear)

    Google Scholar 

  31. Morik, K., Rüping, S.: A Multistrategy Approach to the Classification of Phases in Business Cycles. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) ECML 2002. LNCS (LNAI), vol. 2430, pp. 307–319. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  32. Nakhaeizadeh, G., Schnabl, A.: Development of multi-criteria metrics for the evaluation of data mining algorithms. In: KDD 1997, Newport Beach, CA, August 1997, pp. 37–42. AAAI Press, Menlo Park (1997)

    Google Scholar 

  33. Ng, A., Fu, A.W.c.: Mining frequent episodes for relating financial events and stock trends. In: Whang, K.-Y., Jeon, J., Shim, K., Srivastava, J. (eds.) PAKDD 2003. LNCS (LNAI), vol. 2637, pp. 27–39. Springer, Heidelberg (2003)

    Google Scholar 

  34. Padmanabhan, B., Sen, S., Tuzhilin, A., White, N., Stein, R.: The identification and satisfaction of consumer analysis-driven information needs of marketers on the www. European Journal of Marketing, Special Issue on Marketing in Cyberspace 32 (1998)

    Google Scholar 

  35. Padmanabhan, B., Tuzhilin, A.: A belief-driven method for discovering unexpected patterns. In: KDD 1998, New York City, NY, August 1998, pp. 94–100 (1998)

    Google Scholar 

  36. Pazzani, M., Billsus, D.: Learning and revising user profiles: The identification of interesting web sites. Machine Learning (Kluwer Academic Publishers) 27, 313–331 (1997)

    Article  Google Scholar 

  37. Pěchouček, M., Štěpánková, O., Mikšovský, P.: Maintenance of Discovered Knowledge. In: Żytkow, J.M., Rauch, J. (eds.) PKDD 1999. LNCS (LNAI), vol. 1704, pp. 476–483. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  38. Pensa, R.G., Boulicaut, J.-F.: Boolean property encoding for local set pattern discovery: an application to gene expression data analysis. In: Morik, K., Boulicaut, J.-F., Siebes, A. (eds.) Detecting Local Patterns. LNCS. Springer, Heidelberg (2005) (to appear)

    Google Scholar 

  39. Roddick, J.F., Spiliopoulou, M.: A survey of temporal knowledge discovery paradigms and methods. IEEE Trans. of Knowledge and Data Engineering (August 2002)

    Google Scholar 

  40. Silberschatz, A., Tuzhilin, A.: What makes patterns interesting in knowledge discovery systems. IEEE Trans. on Knowledge and Data Eng. 8(6), 970–974 (December 1996)

    Article  Google Scholar 

  41. Street, W.N., Kim, Y.: A Streaming Ensemble Algorithm (SEA) for Large-Scale Classification. In: Provost, F., Srikant, R. (eds.) Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2001, pp. 377–388. ACM Press, New York (2001)

    Chapter  Google Scholar 

  42. Sun, X., Orlowska, M.E., Zhou, X.: Finding event-oriented patterns in long temporal seuquences. In: Whang, K.-Y., Jeon, J., Shim, K., Srivastava, J. (eds.) PAKDD 2003. LNCS (LNAI), vol. 2637, pp. 15–26. Springer, Heidelberg (2003)

    Google Scholar 

  43. Syed, N.A., Liu, H., Sung, K.K.: Handling Concept Drifts in Incremental Learning with Support Vector Machines. In: Chaudhuri, S., Madigan, D. (eds.) Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, USA, August 1999, pp. 317–321. ACM Press, New York (1999)

    Chapter  Google Scholar 

  44. Tan, P.-N., Kumar, V.: Interestingness Measures for Association Patterns: A Perspective. In: Workshop on Post Processing in Machine Learning and Data Mining at 6th ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining, Boston, USA (August 2000)

    Google Scholar 

  45. Tan, P.-N., Kumar, V., Srivastava, J.: Selecting the Right Interestingness Measure for Association Patterns. In: Proc. of the 8th ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining (2002)

    Google Scholar 

  46. Vazirgiannis, M., Halkidi, M., Gunopoulos, D.: Uncertainty Handling and Quality Assessment in Data Mining. Springer, Heidelberg (2003)

    MATH  Google Scholar 

  47. Widyantoro, D.H., Ioerger, T.R., Yen, J.: An adaptive algorithm for learning changes in user interests. In: Proc. of CIKM 1999, Kansas City, MO, November 1999, pp. 405–412. ACM, New York (1999)

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. Otto-von-Guericke-Universität Magdeburg,  

    Myra Spiliopoulou & Steffan Baron

Authors
  1. Myra Spiliopoulou
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  2. Steffan Baron
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Editor information

Editors and Affiliations

  1. Computer Science VIII, artificial Intelligence Unit, Technische Universität Dortmund, 44221, Dortmund, Germany

    Katharina Morik

  2. INSA-Lyon, LIRIS CNRS UMR5205, F-69621, Villeurbanne, France

    Jean-François Boulicaut

  3. Department of Computer Science, Universiteit Utrecht,  

    Arno Siebes

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Spiliopoulou, M., Baron, S. (2005). Temporal Evolution and Local Patterns. In: Morik, K., Boulicaut, JF., Siebes, A. (eds) Local Pattern Detection. Lecture Notes in Computer Science(), vol 3539. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11504245_12

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  • DOI: https://doi.org/10.1007/11504245_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26543-6

  • Online ISBN: 978-3-540-31894-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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