Titlebook: Knowledge Discovery in Inductive Databases; 5th International Wo Sa?o D?eroski,Jan Struyf Conference proceedings 2007 Springer-Verlag Berli

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Extending the Soft Constraint Based Mining Paradigma tool to drive the discovery process towards potentially . patterns, with the positive side effect of achieving a more efficient computation. So far the research on this paradigm has mainly focussed on the latter aspect: the development of efficient algorithms for the evaluation of constraint-based

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On Interactive Pattern Mining from Relational Databasesnteractive, iterative) nature of pattern discovery. Following the . vision, our framework provides users with an expressive constraint based query language which allows the discovery process to be effectively driven toward potentially interesting patterns. Such constraints are also exploited to redu

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Integrating Decision Tree Learning into Inductive Databasesuery language. The approach that adheres most strictly to this philosophy is probably the one proposed by Calders et al. (2006): in this approach, models are stored in relational tables and queried using standard SQL. The approach has been described in detail for association rule discovery. In this

antenna

Using a Reinforced Concept Lattice to Incrementally Mine Association Rules from Closed Itemsetss to be unwieldy as the frequency requirements become less strict, especially when collected data is highly correlated or dense. Since a big number of the frequent itemsets turns out to be redundant, it is sufficient to consider only the rules among . or .. In order to efficiently generate them, it

Arroyo

從屬

Beam Search Induction and Similarity Constraints for Predictive Clustering Treesn support global models, such as decision trees. Our focus is on predictive clustering trees (PCTs). PCTs generalize decision trees and can be used for prediction and clustering, two of the most common data mining tasks. Regular PCT induction builds PCTs top-down, using a greedy algorithm, similar t

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Bph773

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