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Wesley W. Chu and Kuorong Chiang
University of California at Los Angeles
Los Angeles, CA 90024
e-mail: wwc@cs.ucla.edu
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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