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CFP: A SPECIAL ISSUE OF INFORMATICA ON DATA MINING METRICS 
 
The development of a large number of rule  induction and decision tree
construction algorithms  for  data mining  by researchers  in  machine
learning   and   statistics,  has   seen   empirical   evaluation  and
justification  become  an important   aspect for acceptance  of  newly
developed   algorithms by  researchers in   the field.   To provide  a
comprehensive evaluation, we need a  set of standard criteria such as:
induction  time,  size of  induction   results,  time  to execute  the
induction  results,  and  predicative accuracy.  One algorithm  may be
able to perform better than others with one criterion, but may perform
poorly with other criteria.  With  the same set  of algorithms, we can
also   get different   evaluation   results  from  different  sets  of
databases.  The  question of why,  and  under which  circumstances one
algorithm  (whether  it   is  newly   designed   or an  existing  one)
outperforms  others  becomes   more  important than simply  presenting
empirical results from an arbitrarily selected set of databases.
 
Research  on  data  mining metrics is  based on the  above  mentioned,
widely    adopted   criteria.  These  metrics    also   look into  the
characteristics of the data sets for experiments  such as: the numbers
of classes,  attributes and examples,   the  distribution of  training
examples in the  example space, the level of  noise and the mixture of
continuous and nominal values.  The aim is to develop a meaningful set
of metrics   with  well documented  experiment  results for  different
algorithms. These metrics can be used as a testbed for newly developed
algorithms against existing ones.
 
Original papers are solicited  that describe  research in data  mining
metrics for  a special issue  in Informatica: An International Journal
of Computing and Informatics.  Topics include, but are not limited to,
the following:
 
  - Development of data mining metrics
  - Different knowledge representations and their transformations in
    data mining
  - Generality and complexity of induction results
  - Pre-pruning and post-pruning
  - Deduction of inexact induction results
  - Artificial databases and real world databases: Which are more
    suitable for experimentation?
  - Top-down vs. bottom-up induction algorithms
  - Discretisation of real-valued attributes and fuzzification of
    symbolic values for data mining
  
TIMETABLE

   Papers in 5 hard copies due:    12 August 1996
   Acceptance Notification:       15 October 1996
   Final papers in LaTeX:        15 November 1996
   Publication:                     December 1996
 
GUEST EDITORS
 
  Dr Xindong Wu (xindong@insect.sd.monash.edu.au)
  Department of Software Development, Monash University
  900 Dandenong Road, Caulfield East, Melbourne 3145, Australia
  URL: http://www.sd.monash.edu.au/~xindong/
  Phone: +61 3 9903 1025 Fax: +61 3 9903 1077
 
  Dr Matjaz Gams (matjaz.gams@ijs.si)
  Jozef Stefan Institute, Intelligent Systems Department
  Jamova 39, 61000 Ljubljana, Slovenia
  URL: http://www2.ijs.si/~mezi/matjaz.html
  Phone: +386 61 1773 900 Fax: +386 61 1258 058