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Wesley W. Chu and Kuorong Chiang
University of California at Los Angeles
Los Angeles, CA 90024
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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