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\title{Abstraction of High Level Concepts
                   from Numerical Values in Databases} 
 
\author{Wesley W. Chu and  Kuorong Chiang \\
University of California at Los Angeles \\ 
Los Angeles, CA 90024 \\ 
e-mail: wwc@cs.ucla.edu,  hua@cs.ucla.edu \\
}

 
\begin{document} 
 
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\begin{abstract}
A conceptual clustering method is proposed for discovering high level
concepts of numerical attribute values from databases. The method
considers both {\em frequency} and {\em value} distributions of data,
thus is able to discover relevant concepts from numerical attributes.
The discovered knowledge can be used for representing data
semantically and for providing approximate answers
when exact ones are not available.

Our knowledge discovery approach is to partition the data set of one
or more attributes into clusters that minimize the {\em relaxation
error}.  An algorithm is developed which finds the best binary
partition in $O(n)$ time and generates a concept hierarchy in
$O(n^2)$ time where $n$ is the number of distinct values of the
attribute. The effectiveness of our clustering method is demonstrated
by applying it to a large transportation database for approximate
query answering.
\end{abstract}

\htmladdnormallink{View the Paper (requiring a postscript viewer)}
{http://burton.cs.ucla.edu/tech-docs/kdd94.ps}

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