ITERATE: A Conceptual Clustering Method for Knowledge Discovery in Databases

As the eld of Arti cial Intelligence (AI) matures, researchers are turning more and more to real world applications. With the widespread use of computers, it is estimated that the amount of information collected in the world doubles every 20 months. A substantial amount of this data comes from studying the operations of complex engineering systems (e.g., manufacturing lines, nuclear plants), geological operations in oil and mineral prospecting, data collected by satellites and space missions, and medical data collected from patients and laboratory experiments. It is becoming increasingly important to devise sophisticated schemes for nding interesting concepts and relations between concepts from this large amount of potentially useful data. Frawley, et al.[16] cite examples of a number of forward looking companies that are developing tools and techniques to analyze their databases for interesting and useful patterns. For example, American Airlines uses knowledge discovery techniques to periodically search its frequent yer database to nd pro les of its better customers and

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