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The 31st Annual Conference of the German Classification Society - Gesellschaft für Klassifikation (GfKl), a member of the International Federation of Classification Societies (IFCS), focuses on data analysis, machine learning and their various applications.
The scientific program will include sessions with invited talks and contributed presentations from a broad range of topics. Special emphasis will be laid on interdisciplinary research and the interaction between theory and practice.
The objective of this track is to provide a forum for the exchange of ideas on various aspects of Web and Text Mining and especially its applications for the Semantic Web.
Topics of Interest
- Text Categorization
- Text Segmentation
- Natural Language Processing Methods
- Hypertext Classification
- Analysis of Multilingual Texts
- Cross-language Mining and Retrieval
- Intelligent Web Search
- Information Extraction
- Ontology Learning and Enhancement
- Building and Analyzing the Semantic Web
- Personalization
- Recommendation Engines
- User Modeling
- Customer Profiling
- Web Mining for Business Applications
- Web Usage Mining
- Mining the Deep Web
- Web-based Communities
- Referral Networks
- Visualization Methods
- Adaptive Methods
Track Chairs
Andreas Nürnberger
Otto-von-Guericke University of Magdeburg, Germany
Email: nuernb@iws.cs.uni-magdeburg.de
Myra Spiliopoulou
Otto-von-Guericke University of Magdeburg, Germany
Important Dates
November 10, 2006: | Deadline for abstract submission |
January 12, 2007: | Notification of acceptance |
March 7 - 9, 2007: | Conference |
March 23, 2007: | Submission of full paper for post-conference proceedings |
Submission
All submissions must be done electronically. Detailed information on the submission procedure and the reviewing process for the abstract and for the full paper submissions can be found on the conference website.
Proceedings
For the conference abstract booklets will be provided. Accepted papers will appear in the post-conference proceedings that will be published by Springer-Verlag in the 'Studies in Classification,
Data Analysis, and Knowledge Organization' series.
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