TAILIEUCHUNG - Data Mining and Knowledge Discovery Handbook, 2 Edition part 37

Data Mining and Knowledge Discovery Handbook, 2 Edition part 37. Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data. Data Mining and Knowledge Discovery Handbook, 2nd Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery. | 340 Jean-Francois Boulicaut and Baptiste Jeudy The IDB framework is appealing because it employs declarative queries instead of ad-hoc procedural constructs. As declarative inductive queries are often formulated using constraints inductive querying needs for constraint-based Data Mining techniques and is concerned with defining the necessary constraints. It is useful to abstract the meaning of inductive queries. A simple model has been introduced in Mannila and Toivonen 1997 . Given a language L of patterns . itemsets the theory of a database D . L and a selection predicate C is the set Th D L C y e L C y D true . The predicate selection or constraint C indicates whether a pattern y is interesting or not . y is frequent in D . We say that computing Th D L C is the evaluation for the inductive query C defined as a boolean expression over primitive constraints. Some of them can refer to the behavior of a pattern in the data . its frequency is above a threshold . Frequency is indeed the most studied case of evaluation function. Some others define syntactical restrictions . the length of the pattern is below a threshold and checking them does not need any access to the data. Preprocessing concerns the definition of a mining context D the mining phase is generally the computation of a theory while post-processing is often considered as a querying activity on a materialized theory. To support the whole KDD process it is important to support the specification and the computation of many different but correlated theories. According to this formalization solving an inductive query needs for the computation of every pattern which satisfies C. We emphasized that the model is however quite general beside the itemsets or sequences L can denote . the language of partitions over a collection of objects or the language of decision trees on a collection of attributes. In these cases classical constraints specify some function optimization. If the completeness .

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