Zusammenfassung
Die bisher vorgestellten Data-Mining-Verfahren basieren auf einfachen Datentypen, die sich in natürlicher Weise mit Hilfe des relationalen Datenmodells repräsentieren lassen. In diesem Kapitel werden die Besonderheiten des Data Mining bei zeit-und raumbezogenen Daten sowie bei (Hyper-)Text-Dokumenten diskutiert. Man spricht bei der Anwendung von Data-Mining-Techniken auf diese Datentypen auch von Temporal Data Mining, Spatial Data Mining sowie Text-und Web-Mining. Um den Einblick in diese Gebiete von großer praktischer Bedeutung zu vertiefen, werden einige ausgewählte Verfahren und typische Anwendungen im Detail dargestellt.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Literatur
Brin S., Motwani R., Page L., Winograd T. 1998, „What can you do with a Web in your Pocket?“, Bulletin of the Technical Committee on Data Engeneering, IEEE Computer Society Press, Vol. 21, No. 2, pp. 37–47.
Chakrabarti S., van den Berg M., Dom B. 1999, „Focused Crawling: a new Approach to Topic-Specific Web Resource Discovery“, Proc. International Conference on the World Wide Web (WWW ‘99), siehe auch http://www.almaden.ibm.com/almaden/feat/www8/.
Chakrabarti S., Dom B., Indyk P. 1998, „Enhanced Hypertext Classification Using Hyperlinks“, Proc. ACM SIGMOD Int. Conf on Managament of Data, ACM Press, New York, pp. 307–318.
Chakrabarti S., Dom B., Gibson D., Kleinberg J., Kumar S. R., Raghavan P., Rajagopalan S., Tomkins A. 1999, „Mining the link structure of the World Wide Web“, IEEE Computer, IEEE Computer Society Press, 08/1999, pp. 60–67.
Ester M., Frommelt A., Kriegel H-P., Sander J. 1998, „Algorithms for Characterization and Trend Detection in Spatial Databases“, Proc. 4th Int. Conf. on Knowledge Discovery & Data Mining (KDD’98), AAAI Press, Menlo Park, CA, pp. 44–50.
Ester M., Frommelt A., Kriegel H.-P., Sander J. 2000, „Spatial Data Mining: Database Primitives, Algorithms and Efficient DBMS Support“, Data Mining and Knowledge Discovery, an International Journal, Kluwer Academic Publishers, Vol. 4, Nos.2/3, pp. 193–216.
Feldman R., Dagan I. 1995, „KDT - knowledge discovery in texts“, Proc. 1st Int. Conf. on Knowledge Discovery and Data Mining (KDD’95), AAAI Press, Menlo Park, CA, pp. 112–117.
Gueting R.H. 1994, „An Introduction to Spatial Database Systems “, The VLDB Journal, Vol. 3, No. 4, VLDB Endowment Inc., pp. 357–399.
Hearst M. A. 1999, „Untangling Text Data Mining“, Proc. of the 37th Annual Meeting of the Association for Computational Linguistics (ACL’99), University of Maryland, 20–26 Juni 1999. siehe auch http://www.sims.berkeley.edu/-hearst/papers/ac199/ac199-tdm.html.
Knorr E. M., Ng R. T. 1996, „Finding Aggregate Proximity Relationships and Commonalities in Spatial Data Mining“, IEEE Trans. on Knowledge and Data Engineering, Vol. 8, No. 6, IEEE Computer Society Press, pp 884–897.
Koperski K., Han J. 1995, „Discovery of Spatial Association Rules in Geographic Information Databases“, Proc. 4th Int. Symp. on Large Spatial Databases (SSD ‘85), Lecture Notes in Computer Science, Vol. 951 Springer Verlag, Berlin, pp. 47–66.
Koperski K., Adhikary J., Han J. 1996, „Knowledge Discovery in Spatial Databases: Progress and Challenges“, Proc. SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, Technical Report 96–08, University of British Columbia, Vancouver, Canada, pp. 55–70.
Roddick J. F., Spiliopoulou M. 1999, „Temporal Data Mining: Survey and Issues“, Research Report ACRC-99–007, School of Computer and Information Science, University of South Australia.
Srikant R., Agrawal R. 1996, „Mining Sequential Patterns: Generalizations and Performance Improvements“, Proc. Int. Conf. on Extending Database Technology (EDBT’96). In Lecture Note in Computer Science, Vol. 1057. Springer Verlag, Berlin, pp. 3–17.
Swanson D. R., Smalheiser N. R. 1994, „Assessing a gap in the biomedical literature: Magnesium deficiency and neurologic desease“, Neuroscience Research Communications, Vol. 15, pp. 1–9.
Tkach D. 1998. „Text Mining Technology. Turning Information Into Knowledge“, IBM White Paper zur Text Mining Technologie im IBM Intelligent Miner. http://www-4.ibm.com/software/data/iminer/fortext/download/whiteweb.html
Zamir O., Etzioni O. 1998, „Web Document Clustering: A Feasability Demonstration“, Proc. 21st International ACM SIGIR 98 Conference on Research and Development in Information Retrieval, ACM Press, New York, pp. 46–54.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Ester, M., Sander, J. (2000). Besondere Datentypen und Anwendungen. In: Knowledge Discovery in Databases. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-58331-5_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-58331-5_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67328-6
Online ISBN: 978-3-642-58331-5
eBook Packages: Springer Book Archive