The UCI KDD Archive

[Stephen D. Bay]

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                        The UCI KDD Archive

                         Call for Datasets
                      http://kdd.ics.uci.edu/
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The UC Irvine Knowledge Discovery in Databases (KDD) Archive is a new online 
repository (http://kdd.ics.uci.edu/) of large datasets which encompasses a 
wide variety of data types, analysis tasks, and application areas. The primary 
role of this repository is to serve as a benchmark testbed to enable 
researchers in knowledge discovery and data mining to scale existing and 
future data analysis algorithms to very large and complex data sets. 

This archive is supported by the Information and Data Management Program at the
National Science Foundation, and is intended to expand the current UCI Machine 
Learning Database Repository (http://www.ics.uci.edu/~mlearn/MLRepository.html)
to datasets that are orders of magnitude larger and more complex. 

We are seeking submissions of large, well-documented datasets that can be made 
publicly available. Data types and tasks of interest include, but is not 
limited to: 

         Data Types                 Tasks

         multivariate               classification
         time series                regression
         sequential                 clustering
         relational                 density estimation
         text/web                   retrieval
         image                      causal modeling
         spatial                    visualization
         multimedia                 discovery 
         transactional              exploratory data analysis
         heterogeneous              data cleaning
         sound/audio                recommendation systems
                                         

Submission Guidelines: Please see the UCI KDD Archive web site for detailed 
instructions. 

Stephen Bay (sbay at ics.uci.edu)
librarian