Titlebook: Relational Data Mining; Sa?o D?eroski,Nada Lavra? Book 2001 Springer-Verlag Berlin Heidelberg 2001 Algorithmic Learning.Data Analysis.Data

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Three Companions for Data Mining in First Order Logicoser to attribute value learning and hence more natural. Furthermore, the three systems can be considered first order upgrades of typical data mining systems, which induce association rules, classification rules or decision trees respectively.

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Discovery of Relational Association Rules for relational association rule discovery that are well-suited for exploratory data mining. They offer the flexibility required to experiment with examples more complex than feature vectors and patterns more complex than item sets.

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An Introduction to Inductive Logic Programminggramming and relates logic programming terminology to database terminology. It then defines the task of relational rule induction, the basic data mining task addressed by ILP systems, and presents some basic techniques for solving this task. It concludes with an overview of other relational data mining tasks addressed by ILP systems.

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Propositionalization Approaches to Relational Data Mining reviewing both general-purpose and domaindependent propositionalization approaches from the literature, an extension to the Linus propositionalization method that overcomes the system’s earlier inability to deal with non-determinate local variables is described.

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Relational Data Mining Applications: An Overviews and monitoring, mechanical and traffic engineering. Applications of relational learning are also emerging in business data analysis, text and Web mining, and miscellaneous other fields, such as the analysis of musical performances.

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