Skip to main content
Springer Nature Link
Log in

Data Mining with Calendar Attributes

  • Conference paper
  • First Online:
Temporal, Spatial, and Spatio-Temporal Data Mining (TSDM 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2007))

  • 1023 Accesses

Abstract

This paper addresses the problem of data mining from temporal data based on calendar (date and time) attributes. The proposed methods uses a probabilistic domain generalization graph, i.e., a graph defining a partial order that represents a set of generalization relations for an attribute, with an associated probability distribution for the values in the domain represented by each of its nodes. We specify the components of a domain generalization graph suited to calendar attributes and define granularity, subset, lookup, and algorithmic methods for specifying generalizations between calendar domains. We provide a means of specifying distributions. We show how the calendar DGG can be applied to a data mining problem to produce a list of summaries ranked according to an interest measure given assumed probability distributions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. A. Andrusiewicz and M. E. Orlowska, “On Granularity Factors That Affect Data Mining,” Eighth International Database Workshop, Data Mining, Data Warehousing and Client/Server Databases, Hong Kong, 1997.

    Google Scholar 

  2. C. Bettini, S. Jajodia, and X.S. Wang. Time Granularities in Databases, Data Mining, and Temporal Reasoning. Springer-Verlag, Berlin, 2000.

    MATH  Google Scholar 

  3. C.L. Carter and H.J. Hamilton. Efficient attribute-oriented algorithms for knowledge discovery from large databases. IEEE Transactions on Knowledge and Data Engineering, 10(2):193–208, March/April 1998.

    Article  Google Scholar 

  4. C. Combi, F. Pinciroli, and G. Pozzi. Managing Time Granularity of Narrative Clinical Information: The Temporal Data Model TIME-NESIS. In Proceedings of the Third International Workshop on Temporal Representation and Reasoning (TIME-96), pages 88–93, Key West, Florida, May 1996.

    Google Scholar 

  5. D. Cukierman and J. Delgrande. A language to express time intervals and repetition. In Proceedings of the Second International Workshop on Temporal Representation and Reasoning (TIME-95), pages 41–48, Melbourne, Florida, April 1995.

    Google Scholar 

  6. J. Euzenat. An algebraic approach to granularity in time representation. In Proceedings of the Second International Workshop on Temporal Representation and Reasoning (TIME-95), pages 147–154, Melbourne, Florida, April 1995.

    Google Scholar 

  7. H.J. Hamilton, R.J. Hilderman, and N. Cercone. Attribute-oriented induction using domain generalization graphs. In Proceedings of the Eighth IEEE International Conference on Tools with Artificial Intelligence (ICTAI’96), pages 246–253, Toulouse, France, November 1996.

    Google Scholar 

  8. J. Han, Y. Cai, and N. Cercone. Data-driven discovery of quantitative rules in relational databases. IEEE Transactions on Knowledge and Data Engineering, 5(1):29–40, February 1993.

    Article  Google Scholar 

  9. R. J. Hilderman, H. J. Hamilton, and N. Cercone, Data Mining in Large Databases using Domain Generalization Graphs, Journal of Intelligent Information Systems, vol. 13, pp. 195–234, 1999.

    Article  Google Scholar 

  10. R.J. Hilderman and H.J. Hamilton, Heuristic Measures of Interestingness. In Proceedings of the Third European Conference on the Principles of Data mining and Knowledge Discovery (PKDD’99), pages 232–241, Prague, Czech Republic, September 1999.

    Google Scholar 

  11. J. Hobbs. Granularity. Proc. International Joint Conference on Artificial Intelligence, Los Angles, pages 432–435.

    Google Scholar 

  12. I. Merlo, E. Bertino, E. Ferrari, S. Gadia, G. Guerrini. Querying Multiple Temporal Granularity Data. In Proceedings of the Seventh International Workshop on Temporal Representation and Reasoning (TIME-2000), pages 103–114, Cape Breton, Nova Scotia, Canada, July 2000.

    Google Scholar 

  13. C. P. Rainsford and J. F. Roddick. Adding Temporal Semantics to Association Rules, in Third European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD’99). Prague: Springer, 1999, pp. 504–509.

    Google Scholar 

  14. D. J. Randall, H. J. Hamilton, and R. J. Hilderman. A Technique for Generalizing Temporal Durations in Relational Databases. In Eleventh International FLAIRS Conference (FLAIRS-98), pages 193–197, Sanibel Island, FL, May 1998.

    Google Scholar 

  15. D. J. Randall, H. J. Hamilton, and R. J. Hilderman. Temporal Generalization with Domain Generalization Graphs, International Journal of Pattern Recognition and Artificial Intelligence. 13(2):195–217, 1999.

    Article  Google Scholar 

  16. P. Terenziani. Reasoning about Periodic Events. In Proceedings of the Second International Workshop on Temporal Representation and Reasoning (TIME-95), pages 137–144, Melbourne, Florida, April 1995.

    Google Scholar 

  17. S.-C. Yoon and E. K. Park. An Approach to Intensional Query Answering at Multiple Abstraction Levels using Data Mining Approaches, 32nd Annual Hawaii International Conference on Systems.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Regina, S4S 0A2, Regina, Saskatchewan, Canada

    Howard J. Hamilton & Dee Jay Randall

Authors
  1. Howard J. Hamilton
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dee Jay Randall
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Informatics and Engineering, Flinders University of South Australia, P.O. Box 2100, 5001, Adelaide, South Australia

    John F. Roddick

  2. National Center for Geographic Information and Analysis, University of Maine, ME 04469-5711, Maine, Orono, USA

    Kathleen Hornsby

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Hamilton, H.J., Jay Randall, D. (2001). Data Mining with Calendar Attributes. In: Roddick, J.F., Hornsby, K. (eds) Temporal, Spatial, and Spatio-Temporal Data Mining. TSDM 2000. Lecture Notes in Computer Science(), vol 2007. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45244-3_10

Download citation

  • DOI: https://doi.org/10.1007/3-540-45244-3_10

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41773-6

  • Online ISBN: 978-3-540-45244-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics