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Abstraction of High Level Concepts
from Numerical Values in Databases
Wesley W. Chu and Kuorong Chiang
University of California at Los Angeles
Los Angeles, CA 90024
e-mail: wwc@cs.ucla.edu
Abstract:
A conceptual clustering method is proposed for discovering high level
concepts of numerical attribute values from databases. The method
considers both frequency and 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 relaxation
error. An algorithm is developed which finds the best binary
partition in 
time and generates a concept hierarchy in
time where 
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.
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