PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Giannotti F., Manco G. Making knowledge extraction and reasoning closer. In: PADKK 2000 - Knowledge Discovery and Data Mining, Current Issues and New Applications, 4th Pacific-Asia Conference (Kyoto, Japan, April, 18-20 2000). Proceedings, pp. 360 - 371. (Lecture Notes in Computer Science, vol. 1805). Springer, 2000.
 
 
Abstract
(English)
The paper shows how a logic-based database language can support the various steps of the KDD process by providing a high degree of expressiveness, and the separation of concerns between the specification level and the mapping to the underlying databases and data mining tools. In particular, the mechanism of user-defined aggregates provided in CDC++ allows to specify data mining tasks and to formalize the mining results in a uniform way. We show how the mechanism applies to the concept of Inductive Databases, proposed in [2,12]. We concentrate on bayesian classification and show how user defined aggregates allow to specify the mining evaluation functions and the returned patterns. The resulting formalism provides a flexible way to customize, tune and reason on both the evaluation functions and the extracted knowledge.
Subject knowledge discovery


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