Sonstiges
Semantics, Web and Mining.
2006.
Markus Ackermann, Bettina Berendt, Marko Grobelnik, Andreas Hotho, Dunja Mladenic, Giovanni Semeraro, Myra Spiliopoulou, Gerd Stumme, Vojtech Svatek und Maarten van Someren.
[doi]
[BibTeX]
Tagungsbände
Workshop on Web Mining 2006 (WebMine).
2006.
B. Berendt, A. Hotho, D. Mladenic und G. Semeraro.
[doi]
[BibTeX]
Artikel in Tagungsbänden
TRIAS - An Algorithm for Mining Iceberg Tri-Lattices.
In:
Proc. 6th ICDM conference.
Hong Kong, 2006.
Robert Jäschke, Andreas Hotho, Christoph Schmitz, Bernhard Ganter und Gerd Stumme.
[BibTeX]
Artikel in Zeitschriften
Semantic Web Mining - State of the Art and Future Directions.
Journal of Web Semantics, 4(2):124-143, 2006.
Gerd Stumme, Andreas Hotho und Bettina Berendt.
[doi]
[Kurzfassung]
[BibTeX]
SemanticWeb Mining aims at combining the two fast-developing research areas SemanticWeb andWeb Mining.
This survey analyzes the convergence of trends from both areas: an increasing number of researchers is working on
improving the results ofWeb Mining by exploiting semantic structures in theWeb, and they make use ofWeb Mining
techniques for building the Semantic Web. Last but not least, these techniques can be used for mining the Semantic
Web itself.
The Semantic Web is the second-generation WWW, enriched by machine-processable information which supports
the user in his tasks. Given the enormous size even of today’s Web, it is impossible to manually enrich all of
these resources. Therefore, automated schemes for learning the relevant information are increasingly being used.
Web Mining aims at discovering insights about the meaning of Web resources and their usage. Given the primarily
syntactical nature of the data being mined, the discovery of meaning is impossible based on these data only. Therefore,
formalizations of the semantics of Web sites and navigation behavior are becoming more and more common.
Furthermore, mining the Semantic Web itself is another upcoming application. We argue that the two areas Web
Mining and Semantic Web need each other to fulfill their goals, but that the full potential of this convergence is not
yet realized. This paper gives an overview of where the two areas meet today, and sketches ways of how a closer
integration could be profitable.
Artikel in Tagungsbänden
Exploratory Mining and Pruning Optimizations of Constrained Association Rules..
In:
SIGMOD Conference, Seiten 13-24.
1998.
Raymond T. Ng, Laks V. S. Lakshmanan, Jiawei Han und Alex Pang.
[doi]
[BibTeX]
Sonstiges
Advances in Knowledge Discovery and Data Mining..
1996.
[doi]
[BibTeX]
Artikel in Zeitschriften
An effective hash-based algorithm for mining association rules.
Proceedings of the 1995 ACM SIGMOD international conference on Management of data:175-186, 1995.
J.S. Park, M.S. Chen und P.S. Yu.
[BibTeX]
Artikel in Tagungsbänden
Fast Algorithms for Mining Association Rules in Large Databases.
In:
VLDB '94: Proceedings of the 20th International Conference on Very Large Data Bases, Seiten 487-499.
Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1994.
Rakesh Agrawal und Ramakrishnan Srikant.
[BibTeX]