An Error-based Conceptual Clustering Method for Providing Approximate Query Answers

Wesley W. Chu, Kuorong Chiang, Chih Cheng Hsu, Henrick Yau

研究成果: 雜誌貢獻期刊論文同行評審

19 引文 斯高帕斯(Scopus)


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 it 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. Efficient clustering algorithms are developed which can be recursively called to generate a concept hierarchy. Applications of such clustering method to structured data and feature-based image are given. The effectiveness of our clustering method is demonstrated by applying it to a large transportation database for approximate query answering.

頁(從 - 到)216
期刊Communications of the ACM
出版狀態已出版 - 1 12月 1996


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