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• W2336675426 abstract "The general idea of discovering in large amounts of data is both appealing and intuitive. Typically we focus our attention on learning algorithms, which provide core capability of generalizing from large numbers of small, very specific facts to useful high-level rules; these learning techniques seem to hold most excitement and perhaps most substantive scientific content in discovery in databases (KDD) enterprise. However, when we engage in real-world discovery tasks, we find that they can be extremely complex, and that induction of rules is only one small part of overall process. While others have written overviews of the concept of KDD, and even provided block diagrams for knowledge discovery systems, no one has begun to identify all of building blocks in a realistic KDD process. This is what we attempt to do here. Besides bringing into discussion several parts of process that have received inadequate attention in KDD community, a careful elucidation of steps in a realistic discovery process can provide a framework for comparison of different technologies and tools that are almost impossible to compare without a clean model." @default.
• W2336675426 created "2016-06-24" @default.
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• W2336675426 date "1994-01-01" @default.
• W2336675426 modified "2023-09-27" @default.
• W2336675426 title "The Process of Knowledge Discovery in A First Sketch Databases" @default.
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