2/1 BISC Seminar talk


Subject: 2/1 BISC Seminar talk
From: Michelle T. Lin (michlin@cs.berkeley.edu)
Date: Fri Jan 28 2000 - 19:10:26 MET


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Berkeley Initiative in Soft Computing (BISC)
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 !! DAY CHANGE: BISC Seminar on Tuesdays during entire semester !!

   BISC SEMINAR

   310 Soda Hall
   Tuesday, Feburary, 1st, 2000
   4pm-5pm
 
   DATA MINING: A FUZZY LOGIC BASED APPROACH
   =========================================

   Michael R. Berthold

   Berkeley Initiative in Soft Computing
   University of California at Berkeley
   Berkeley, CA 94720, USA
   eMail: berthold@cs.berkeley.edu

   ABSTRACT: Automatic Data Mining has started to raise increasing attention,
especially in areas where a large amount of data is gathered automatically
and manual analysis is not feasible anymore. Also applications where data is
recorded online without a possibility for continuous analysis are demanding
for automatic approaches. Examples include such diverse applications as the
automatic monitoring of patients in medicine (which requires an understanding
of the underlying behavior), optimization of industrial processes, and also
the extraction of expert knowledge or other human behaviour from observations
of their (inter)actions.
Techniques from diverse disciplines have been developed or rediscovered
recently, resulting in an increasing set of tools to automatically analyze
such data. Most of these tools, however, require the user to have detailed
knowledge about the tools' underlying algorithms, to fully make use of their
potential. In order to offer the user the possibility to explore the data,
unrestricted by a specific tool's limitations, it is necessary to provide
easy to use, quick ways to give the user first insights. In addition the
extracted knowledge has to be presented to the user in an understandable
manner, enabling interaction and refinement of the focus of analysis.

  In this talk I will discuss some of the methods we have developed recently
that aim towards the use of fuzzy models for an efficient and easily usable
methodology to build interpretable models from data. I will demonstrate how
these models are constructed from examples and how the resulting fuzzy rules
summarize the extracted knowledge. In addition I will briefly discuss how
such models can be used to point out potential outliers, that is, examples
that would otherwise interfere with model generation. I will conclude with a
brief discussion of more recent work on hierarchical models and visualization
of fuzzy models which promise to increase applicability of our data analysis
methods to handle also extremely large amounts of data successfully.

( Parts of this talk are based on joint work with Prof. Lawrence O. Hall from
  the University of South Florida )

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