Semantics, Web and Mining
Ackermann, M.; Berendt, B.; Grobelnik, M.; Hotho, A.; Mladenic, D.; Semeraro, G.; Spiliopoulou, M.; Stumme, G.; Svatek, V. & van Someren, M.
2006 [pdf]
Workshop on Web Mining 2006 (WebMine)
2006, Berendt, B.; Hotho, A.; Mladenic, D. & Semeraro, G., ed. [pdf]
TRIAS - An Algorithm for Mining Iceberg Tri-Lattices
Jäschke, R.; Hotho, A.; Schmitz, C.; Ganter, B. & Stumme, G.
, 'Proc. 6th ICDM conference', Hong Kong, [] (2006)
Semantic Web Mining - State of the Art and Future Directions
Stumme, G.; Hotho, A. & Berendt, B.
Journal of Web Semantics, 4(2) 124-143 (2006) [pdf]
SemanticWeb Mining aims at combining the two fast-developing research areas SemanticWeb andWeb Mining.
is survey analyzes the convergence of trends from both areas: an increasing number of researchers is working on
proving the results ofWeb Mining by exploiting semantic structures in theWeb, and they make use ofWeb Mining
chniques for building the Semantic Web. Last but not least, these techniques can be used for mining the Semantic
b itself.
e Semantic Web is the second-generation WWW, enriched by machine-processable information which supports
e user in his tasks. Given the enormous size even of today’s Web, it is impossible to manually enrich all of
ese resources. Therefore, automated schemes for learning the relevant information are increasingly being used.
b Mining aims at discovering insights about the meaning of Web resources and their usage. Given the primarily
ntactical nature of the data being mined, the discovery of meaning is impossible based on these data only. Therefore,
rmalizations of the semantics of Web sites and navigation behavior are becoming more and more common.
rthermore, mining the Semantic Web itself is another upcoming application. We argue that the two areas Web
ning and Semantic Web need each other to fulfill their goals, but that the full potential of this convergence is not
t realized. This paper gives an overview of where the two areas meet today, and sketches ways of how a closer
tegration could be profitable.
Exploratory Mining and Pruning Optimizations of Constrained Association Rules.
Ng, R. T.; Lakshmanan, L. V. S.; Han, J. & Pang, A.
, 'SIGMOD Conference', 13-24 (1998) [pdf]
Advances in Knowledge Discovery and Data Mining.
1996, Fayyad, U. M.; Piatetsky-Shapiro, G.; Smyth, P. & Uthurusamy, R., ed., AAAI/MIT Press [pdf]
An effective hash-based algorithm for mining association rules
Park, J.; Chen, M. & Yu, P.
Proceedings of the 1995 ACM SIGMOD international conference on Management of data 175-186 (1995)
Fast Algorithms for Mining Association Rules in Large Databases
Agrawal, R. & Srikant, R.
, 'VLDB '94: Proceedings of the 20th International Conference on Very Large Data Bases', Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 487-499 (1994)