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

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.