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From: jair-ed@ptolemy.arc.nasa.gov
Subject: New Article, Iterative Optimization and Simplification ...
Message-ID: <1996Apr16.233415.21002@ptolemy-ethernet.arc.nasa.gov>
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Date: Tue, 16 Apr 1996 23:34:15 GMT
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JAIR is pleased to announce the publication of the following article:

Fisher, D. (1996)
  "Iterative Optimization and Simplification of Hierarchical Clusterings", 
   Volume 4, pages 147-178.

   Available in HTML, Postscript (286K) and compressed Postscript (130K).
   For quick access via your WWW browser, use this URL:
     http://www.cs.washington.edu/research/jair/abstracts/fisher96a.html
   More detailed instructions are below.

   Abstract: Clustering is often used for discovering structure in data.
   Clustering systems differ in the objective function used to evaluate
   clustering quality and the control strategy used to search the space
   of clusterings. Ideally, the search strategy should consistently
   construct clusterings of high quality, but be computationally
   inexpensive as well. In general, we cannot have it both ways, but we
   can partition the search so that a system inexpensively constructs a
   `tentative' clustering for initial examination, followed by iterative
   optimization, which continues to search in background for improved
   clusterings. Given this motivation, we evaluate an inexpensive
   strategy for creating initial clusterings, coupled with several
   control strategies for iterative optimization, each of which
   repeatedly modifies an initial clustering in search of a better
   one. One of these methods appears novel as an iterative optimization
   strategy in clustering contexts. Once a clustering has been
   constructed it is judged by analysts -- often according to
   task-specific criteria. Several authors have abstracted these criteria
   and posited a generic performance task akin to pattern completion,
   where the error rate over completed patterns is used to `externally'
   judge clustering utility. Given this performance task, we adapt
   resampling-based pruning strategies used by supervised learning
   systems to the task of simplifying hierarchical clusterings, thus
   promising to ease post-clustering analysis. Finally, we propose a
   number of objective functions, based on attribute-selection measures
   for decision-tree induction, that might perform well on the error rate
   and simplicity dimensions.

The article is available via:
   
 -- comp.ai.jair.papers (also see comp.ai.jair.announce)

 -- World Wide Web: The URL for our World Wide Web server is
       http://www.cs.washington.edu/research/jair/home.html
    For direct access to this article and related files try:
       http://www.cs.washington.edu/research/jair/abstracts/fisher96a.html

 -- Anonymous FTP from either of the two sites below.

    Carnegie-Mellon University (USA):
	ftp://p.gp.cs.cmu.edu/usr/jair/pub/volume4/fisher96a.ps
    The University of Genoa (Italy):
	ftp://ftp.mrg.dist.unige.it/pub/jair/pub/volume4/fisher96a.ps

    The compressed PostScript file is named fisher96a.ps.Z (130K)

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