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An important aspect of data mining algorithms and systems is that they should scale well to large databases. A consequence of this is that most data mining tools are based on machine learning algorithms that work on data in attribute-value format. Experience has proven that such 'Single-table' mining algorithms indeed scale well. This paper presents a framework and an architecture that provide such a generalization. In this framework the semantic information in the database schema, e.g., foreign keys are exploited to prune the search space and, in the architecture, database primitives are defined to ensure efficiency.

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Format:PDFSize:60 KB
Date:Jan 2008
Pages:12

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