Citation Request:
   The Stalog databases are a subset of the datasets used in the
   European Statlog project.  Any publications based on these datasets
   should include an acknowledgment of the original sources of the
   datasets.

1. Title: Statlog Databases

2. Sources: 
    (a) See Above.
    (b) Donors: Ross D. King
                Department of Statistics and Modelling Science
                University of Strathclyde
                Glasgow G1 1XH
                Scotland
                U.K.
                +44 41 552-4400 x 3033
                Fax +44 41 552-4711
                ross@turing.uk.ac
   (c) Date: October 1992

3. Past Usage:
   1. Feng,C., Sutherland,A., King,S., Muggleton,S. & Henery,R.
      (1993). Comparison of Machine Learning Classifiers to 
      Statistics and Neural Networks. AI & Stats Conf. 93.
   
4. Relevant Information:
   The databases available here were in used in the European StatLog
   project, which involves comparing the performances of machine learning,
   statistical, and neural network algorithms on data sets from real-world
   industrial areas including medicine, finance, image analysis,
   and engineering design.  Not all of the databases used in the
   project are available in this repository.

5. Databases:

   (a) Vehicle Silhouettes:
       The original purpose was to find a method of distinguishing
       3D objects within a 2D image by application of an ensemble of
       shape feature extractors to the 2D silhouettes of the objects.

   (b) Landsat Satellite:
       The database consists of the multi-spectral values
       of pixels in 3x3 neighbourhoods in a satellite image,
       and the classification associated with the central pixel
       in each neighbourhood. The aim is to predict this
       classification, given the multi-spectral values. In
       the sample database, the class of a pixel is coded as
       a number.

   (c) Shuttle:
       The shuttle dataset contains 9 attributes all of which are 
       numerical. Approximately 80% of the data belongs to class 1.

   (d) Australian Credit Approval:
       This file concerns credit card applications.  All attribute names
       and values have been changed to meaningless symbols to protect
       confidentiality of the data.  This database exists elsewhere in
       the repository (Credit Screening Database) in a slightly different
       form.

   (e) Heart Disease:
       This dataset is a heart disease database similar to a database
       already present in the repository (Heart Disease databases)
       but in a slightly different form.  This database contains 13 
       attributes (which have been extracted from a larger set of 75).

   (f) Image Segmentation:
       This dataset is an image segmentation database similar to a database
       already present in the repository (Image segmentation database)
       but in a slightly different form.  The instances were drawn randomly 
       from a database of 7 outdoor images.  The images were handsegmented 
       to create a classification for every pixel.  Each instance is a 3x3 
       region.

   (g) German Credit:
       This dataset classifies people described by a set of attributes as
       good or bad credit risks.  Comes in two formats (one all numeric).
       Also comes with a cost matrix.